Ann Surg Oncol (2016) 23:975–988 DOI 10.1245/s10434-015-4924-2
REVIEW ARTICLE – GYNECOLOGIC ONCOLOGY
Predictive Modeling: A New Paradigm for Managing Endometrial Cancer Sofiane Bendifallah, MD1,2, Emile Daraı¨, MD, PhD1,3, and Marcos Ballester, MD, PhD1,3 1
Department of Gynecology and Obstetrics, Tenon University Hospital, Assistance Publique des Hoˆpitaux de Paris (APHP), University Pierre and Marie Curie, Institut Universitaire de Cance´rologie (IUC), Paris 6, France; 2INSERM UMR S 707, ‘‘Epidemiology, Information Systems, Modeling,’’, University Pierre and Marie Curie, Paris 6, France; 3INSERM UMR S 938, University Pierre et Marie Curie, Paris 6, France
ABSTRACT With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.
Endometrial Cancer (EC) is the most frequent gynecologic cancer in developed countries and ranks overall as the fourth most common cancer in woman (after breast, lung and bronchus, and colon and rectum).1 Three-quarters of patients are diagnosed at an early stage (International Federation of Gynecology and Obstetrics [FIGO] stage I or II disease), with a 5-year overall survival (OS) ranging Society of Surgical Oncology 2015 First Received: 17 September 2015; Published Online: 17 November 2015 S. Bendifallah, MD e-mail: [email protected]
from 74 to 91 %.2–4 In contrast, advanced disease stages are associated with a reported 5-year OS of 57–66 % for FIGO stage III and 20–26 % for FIGO stage IV disease.4 Over the past decade, new options in diagnostic and treatment modalities have created a shift in the medical decision process for EC.5–12 This has given rise to the emergence of individualized medicine, where each woman with EC can benefit from an individualized and customized treatment approach.8,13–15 Currently, the clinician needs to consider imaging techniques, surgical options, new adjuvant drugs, and the patient’s quality of life—as well as, likely in the near future, genomic data.5,6,8,10,11,16,17 Such a combination of factors renders clinical decision making difficult, even confusing at the individual level. To overcome such limitations, a new paradigm has emerged that is based on the implementation of predictive, personalized, preventive, and participatory models.15,18,19 Various predictive tools (algorithms, nomograms, and risk scoring systems [RSS]) have been developed.20–29 However, to our knowledge, no standardized assessment of their robustness, reproducibility, or clinical utility has been formally reported.15,19,30 In this review, we highlight existing models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. CURRENT CLINICAL CLASSIFICATIONS OF ENDOMETRIAL CANCER The most adopted classifications are the 2009 FIGO2 and the tumor, node, metastasis (TNM) classification systems, which are based on surgical staging and which stratifies cancer according to the local extent of the tumor (T), whether the cancer has spread to lymph node (LN) (N),
976
S. Bendifallah et al.
TABLE 1 Description of six classifications systems currently used in EC management Classification
Year of publication
No. of patients
Criteria included
TNM, FIGO2
2009
–
• Extent of tumor (T) • Spread to lymph nodes (N) • Spread to distant sites (M)
PORTEC-133
2000
715
• Age • Histologic type (endometrial adenocarcinoma) • Histologic grade (1, 2, 3)
GOG-9934
2004
382
• Myometrial invasion (C50 and \50 %) • Age • Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • FIGO stage
SEPAL35
2010
671
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • FIGO stage • LVSI
ESMO
10
2013
–
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • FIGO stage for myometrial invasion (C50 and \50 %)
ESMO modified36
2014
496
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • LVSI
ESMO European Society of Medical Oncology, FIGO International Federation of Gynecology and Obstetrics, GOG Gynecologic Oncology Group, LVSI lymphovascular space involvement, PORTEC Post Operative Radiation Therapy in Endometrial Carcinoma, SEPAL Survival Effect of Para-Aortic Lymphadenectomy in Endometrial Cancer, TNM tumor, node, metastasis classification system
and whether it has spread to distant sites (M).2,31,32 To overcome the existing classification limitations, prognostic factors have been aggregated and added to several RSS, namely the Post Operative Radiation Therapy in Endometrial Carcinoma (PORTEC) 1 classification, the Gynecologic Oncology Group (GOG) 99 classification, the Survival Effect of Para-Aortic Lymphadenectomy in Endometrial Cancer (SEPAL) classification, the European Society of Medical Oncology (ESMO) classification, and the ESMO modified classification—all of which are currently used worldwide to guide decision making and clinical trial design (Table 1).10,33–37 Although the core variables of these classifications are similar, it appears that for major classifications, most have never been externally validated, accuracy is not reported, and only one simultaneous comparison using the same cohort has been performed.10,32–36,38,39
METHODOLOGIC PROCESSES FOR MODELING PREDICTION TOOLS We provide a schematic overview of the methodologic processes for predictive model development, describing model development and assessing its clinical usefulness, in Fig. 1.40–44,68 ENDOMETRIAL CANCER CARE CHALLENGES Surgery plays a major role in the management of EC, especially in patients with early-stage disease. However, practice patterns vary widely according to national and international guidelines.9–12,35,45 This is mainly due to several criteria defining risk groups for recurrence, unstandardized protocols for surgical staging, and different indications for adjuvant therapies.10,33,34,46–48
Predictive Models in Endometrial Cancer
977
DATA SELECTION
FIRST STEP
(Litterature review, hypothesis, inclusion criteria, eligibility) Population of interest
Primary outcome
Predictors
-
Single institution
-
Binary (presence/absence)
-
Clinical evidence (review)
-
Multiple centers
-
Censored outcome (Time
-
Statistical significance
to event)
CONSTRUCTION OF THE MODEL (On training data set) Analysis and Testing predictors
Select the Model
-
-
Logistic regression
Statistical significance expressed as P value
model for binary
SECOND STEP
Threshold for clinical decision making
outcome -
-
prior publication
Cox proportional hazard model for
-
censored outcome
-
On clinical evidence
-
On statistical significance
Clinical significance according to
Predictors interactions and multicollinearity
Nomogram and Algorithm Graphical or a mathematical tools: the effect of predictors on the outcome of interest is represented either in the format of axes, and risk points are attributed or in mathematical formula Stratification Scoring System Risk grouping tools which place similar, though not identical, patients into the same group and attempt to predict an individual prognosis based on the behaviour of the group
THIRD STEP
VALIDATION of PREDICTIVE ACCURACY (External data set or internal statistical approach) Discrimination -
The area under the receiver operating characteristic curve indicates the predictive properties of a model and quantifies the overall accuracy
Generalizability -
General applicability for clinical practice
Level of complexity
Calibration -
Calibration plots show the relationship between predicted and observed probabilities of the outcome of interest. Clinical utility
FIG. 1 Schematic overview of methodological processes for predictive model development
Optimal Lymph Node Assessment: The Place of Predictive Models Selecting patients who might benefit from systematic complete nodal staging is a major issue to guide postoperative treatment in patients with early-stage EC.7,47,49,50 Indeed, LN status is one of the most important prognostic factors (Table 2).4,51–54
In this area of research, a meta-analysis of two randomized trials on the impact of systematic lymphadenectomy in early-stage EC showed no benefit on overall and recurrence-free survival.47,49,55 Despite the unestablished therapeutic role of lymphadenectomy, it remains the most reliable method to evaluate LN status, providing important prognostic information and assisting in the tailoring of adjuvant therapies. However, complete
Type of predictive model
Selecting women for secondary LND
Selecting women for secondary LND
Selecting women for secondary LND
Nomogram MLR
Nomogram MLR
Algorithm
AlHilli et al. 201321
Bendifallah et al.201223
Kamura et al. 199924
MLR
To determine extent of LND
FIGO 2009 stages I–IV
175
FIGO 1988 stages I–IV
18,294 FIGO 2009 stages I–III
883
397
Care objective No. of FIGO women stage
Nomogram MLR
Statistical model
Kang et al. 201459
Postoperative predictive model
Study
TABLE 2 Predictive models for lymph node status in EC
Type 1
• Women treated between 1979 and 1994
Hospital
• Retrospective Total HT, ±P/PA • Kyushu LND University
LNM
Type 1 and • Retrospective Total HT, LNM and P/ 2 • U.S. National PA LND Cancer Institute data base from 1988 and 2007
Type 1
Defined as nodal metastasis when P/PA LND was performed or P/PA lymph node recurrence after negative LND or when LND was not performed
Outcome
• Retrospective Total HT, ±P/PA • Mayo Clinic LND • Women treated between 1999 and 2008
Treatment
Para aortic metastasis
Cohort study
Type 1 and • Retrospective Total HT, 2 P/PA • Multicenter LND
Pathologic type
Internal
Validation
Internal validation, 0.80 (95 % CI 0.79– 0.81); external validation by Bendifallah et al.
Internal and external
0.88 and 0.87 Internal and for model external including (Bendifallah TD and et al. 2013) model without TD, respectively
0.87
Concordance index
Sensitivity, NA specificity, and accuracy were 83, 72, • Macroscopic and 73 %, tumor diameter respectively
• Microscopic degree of myometrial invasion
• Location of tumor
• Histologic subtype
• Grade
• Race
• Age
• LVSI • ±TD
• Cervical stromal invasion
• FIGO grade
• Myometrial invasion
• CA-125
• LVSI
• Pathologic type
• Myometrial invasion
Predictors
978 S. Bendifallah et al.
Type of predictive model
Statistical model
MLR
MLR
Lee et al. 201225 Algorithm
Algorithm
181
Selecting 214 women for LND with risk stratification
Preoperative 360 selection of women with low-Risk of LNM
Selecting 774 women with advanced disease
Selecting women for LND
Pathologic type
Cohort study
Treatment
Outcome
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
FIGO 2009 stages I–IV
• Women treated between 1993 and 2000
Total Preoperative Type 1 and • Multicenter HT ± P/ prediction 2 • Hokkaido and PA LND of LNM 2 affiliated hospitals
Type 1 and • Retrospective Total HT Preoperative and P/ prediction 2 • Multicenter PA LND of LNM • Women treated between 2002 and 2008
• Women treated between 2008 and 2012
Type 1 and • Retrospective Total HT Preoperative and P/ prediction 2 • Helsinki PA LND of LNM University Central Hospital
Presumed Type 1 and • Retrospective Total HT Preoperative FIGO and P/ prediction 2 • Multicenter 2009 IPA LND of LNM population II • Women treated between 2000 and 2010
Care objective No. of FIGO women stage
Validation
• Serum CA125 level
• Histologic grade
• Volume index
Internal and external by Todo et al. (2007)
NA
• 3 MRI parameters • Histologic type
Internal
0.85 (95 % CI Internal and 0.79–0.92) external by Kang et al. (2013)
0.823
0.77 (95 % CI Internal 0.70–0.83)
Concordance index
• Serum CA125 levels
• High-risk histology
• Elevated serum CA-125
• Thrombocytosis
• Leukocytosis
• Primary site tumor
• Histologic subtype
• Grade
• Race
• Age
Predictors
CI confidence interval, FIGO International Federation of Gynecology and Obstetrics, LND lymph node dissection, LNM lymph node metastasis staging system, LVSI lymphovascular space involvement, MLR multiple linear regression, NA not applicable, HT total hysterectomy, P/PA LND pelvic and/or para-aortic lymph node dissection, TD tumor diameter
Todo et al. 200726
MLR
Algorithm
Luomarantaet al. 201356
Preoperative predictive model Koskas et al. Algorithm MLR 201457
Study
TABLE 2 continued
Predictive Models in Endometrial Cancer 979
980
pelvic and/or para-aortic lymphadenectomy surgery is associated with an increased risk of morbidity, such as lymphocyst or lymphoedema.47,49 Several authors have reported preoperative predictive models of LN status, such as algorithms or nomograms, as a more reliable way of promoting a personalized therapeutic strategy that may help decide the extent of surgical staging.25,26,56,57 In 2013, Luomaranta et al. proposed a preoperative algorithm to predict the probability of LN and distant metastasis in patients with EC undergoing systematic pelvic and/or para-aortic lymphadenectomy.56 The final score system was obtained by combining leukocytosis, thrombocytosis, CA-125 values, and High Risk (HR) histology. However, this predictive model has not been externally validated since its publication. Moreover, there is some concern about the way in which the parameters were assigned. Similarly, Koskas et al. also proposed an algorithm focusing on patient with early-stage EC for whom indication for lymphadenectomy is still a matter of debate.57 The authors combined preoperative tumor characteristics provided by endometrial sampling (i.e., histologic subtype and grade) and magnetic resonance imaging information (depth of myometrial invasion and extension to cervical stroma) to predict LN status. This algorithm showed good discrimination and was well calibrated. Both these predictive models represent a valuable contribution that may improve preoperative EC management. Moreover, they are limited by a lack of external validation and a considerable level of complexity. Lee et al. developed a preoperative model to identify a low risk (LR) group before surgery for whom nodal staging should not be performed.25 However, although it has been shown to be exportable, the major concern of this model is its clinical relevance. This is because the most relevant question for early-stage EC is not determining who should be spared pelvic and/or para-aortic lymphadenectomy but rather which patient has an increased risk of LN metastasis. Moreover, there is seemingly little correlation between LN diameter and the presence of LN metastases.10 Similarly, Todo et al. described a preoperative model based on tumor volume measured by magnetic resonance imaging, serum CA-125 level, histologic type, and grade.26 In contrast with previous models, those two predictive models were externally validated with a demonstrated statistical accuracy. Although the Todo algorithm improves patient selection, there is some concern about the robustness of the external validation—both patient populations were of Asian origin—raising the issue of its applicability in European or American populations. Furthermore, the fairly complex nature of the model renders it difficult to use in routine practice. Finally, the rate of LN metastases corresponds to the metastasis rate reported in risk groups of the current classifications, which limits the interest of the model.37 The model based on final histology
S. Bendifallah et al.
was designed by Kamura et al. from a cohort of 175 patients with EC from the Kyushu University Hospital who were treated by hysterectomy combined with pelvic LN dissection.24 The major interest of such a formula lies in the evaluation of a cutoff point and the ability to assess these parameters intraoperatively. However, this predictive model has not yet been externally validated. In 2012, Bendifallah et al. suggested a nomogram based on pathologic hysterectomy characteristics to provide a more individualized estimation of LN metastasis in stage FIGO I–IIIc type 1 and 2 EC.23 Although, this nomogram is based on a solid methodology with the use of internal and external validation sets, multicenter data, and clinically relevant variables, it does not take into account the subset of type 1 EC; it lacks a few major predictive factors, such as the lymphovascular space involvement (LVSI) and tumor diameter; and there is no optimal decision threshold. As an alternative, AlHilli et al. developed a nomogram for the prediction of lymphatic dissemination after hysterectomy focusing on type 1 EC.22 Two nomograms were designed (a full and an alternative model) with and without the tumor diameter as a predictive factor associated with classic predictors. Myometrial invasion, tumor diameter, FIGO grade, cervical stromal invasion, and LVSI were included in the nomogram. Although this nomogram is relevant because of the specific patient population and the inclusion of major predictive factors such as LVSI and tumor diameter, the external validation study provided by Bendifallah et al. in 2013 showed that the tools were only partly generalizable to a new and independent patient population.58 Therefore, although these tools provide a more individualized estimation of LN dissemination, additional parameters are needed to allow higher accuracy for counselling patient in clinical practice. Finally, Kang et al. developed a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with EC.59 The tool was designed to determine the extent of lymphadenectomy after incomplete surgery but no external validation study was performed. More recently, Bendifallah et al. designed an RSS for predicting LN metastases in patients with earlystage EC.28 LN metastases were associated with five variables: age C60 years, histologic grade 3 and/or type 2 disease, primary tumor diameter C1.5 cm, depth of myometrial invasion C50 %, and positive LVSI status. A total score of 6 points corresponded to the optimal threshold of the RSS, with a rate of LN metastases of 7.5 and 34.7 % for LR (B6 points) and HR patients ([6 points), respectively. At this threshold, the diagnostic accuracy was 83 %. This RSS could be useful in clinical practice to determine which patients with early-stage EC should benefit from secondary surgical staging including complete lymphadenectomy.
1240
193
925
356
Nomogram CPHM
Nomogram CPHM
Lakhman et al. 201465
AlHilli et al. Nomogram CPHM 201463
AlHilli et al. Nomogram CPHM 201463
FIGO 2009 stages I–IV
FIGO 2009 stages I–IV
FIGO 2009 stages I–IV
Early stages
No. of FIGO women stage
Creutzberg et al. 201564
Statistical model
Predictive model
Study
3 and 5 y OS • Age
High risk: grade 3, and nonendometrioid EC • Mayo Clinic
Total HT ± P/PA LND ± EBRT ± chemotherapy • Women treated ± brachytherapy between 1999 and 2008
• Retrospective
• Retrospective Low-risk: FIGO Total HT ± P/PA grades 1 and 2 LND ± EBRT ± • Mayo Clinic endometrioid EC chemotherapy • Women treated ± brachytherapy between 1999 and 2008
Total HT ± P/PA LND ± • Memorial chemotherapy Sloan-Kettering ± radiation Cancer Center
• Para aortic nodal status
• Adjuvant therapy
• Cervical stromal invasion
• LVSI
• FIGO 2009 stage
• ASA
3 and 5 y OS • Age
• Pelvic node status
• Postoperative complication
• Primary TD
• FIG0 2009 stage
• Cardiovascular disease
• Pulmonary dysfunction
3 and 5 y OS • Age
• Omental implants
• Ascites
• Myometrial invasion
• LVSI
• Grade
• 2009 FIGO stage
• Retrospective
0.759
0.803
0.64
Internal
Internal
Internal
DR (0.73), DFS External (0.69), OS (0.70), LRR (0.59)
• Age
Validation
Concordance index
Predictors
Type 2
Outcome
LRR, DR, OS, and DFS
Treatment
• Pooled cohort Total HT ± P/PA LND ± none ± from PORTECEBRT ± VBT 1 and PORTEC-2
Cohort study
Type 1
Pathologic type
TABLE 3 Available predictive models for recurrence and survival in EC
Predictive Models in Endometrial Cancer 981
Predictive model
Statistical model
2097
Nomogram CPHM
Kondalsamy et al. 201229
FIGO 2009 stages I–III
FIGO 2009 stages I–III
FIGO 2009 stages I–III
FIGO 1988 stages I–IV Not specified
• Prospective
3 y distant • Age recurrence • 2009 FIGO stage
Primary surgery (NS) ± • Multicenter EBRT ± • Women treated brachytherapy ± between 1997 chemotherapy ± and 2007 hormone therapy
• Retrospective
Type 1 and 2
Primary surgery ± EBRT ± brachytherapy ± • Women treated chemotherapy ± between 1997 hormone therapy and 2007
3 y isolated LRR
• Retrospective
• Peritoneal washing
• Histologic type
• LVSI
• Grade
• Myometrial invasion
• Histologic type
• LVSI
• Grade
• 2009 FIGO stage
• Age
• LVSI
• Grade
• Tumor diameter
3y • Age recurrence • Myometrial invasion
• Histologic subtype
• Final grade
• 1988 FIGO Stage
• Number of negative lymph nodes
Type 1 and 2
• Multicenter
Concordance index
0.86
0.73
0.74 (95 % CI 0.71–0.77)
• Age at diagnosis 0.746 ± 0.011
Predictors
Total HT ± P/PA LND ± EBRT ± • Multicenter chemotherapy ± • Women treated brachytherapy between 2001 and 2012
3 y overall survival
Outcome
• Retrospective
• Women treated between 1988 and 2002
• Memorial Sloan-Kettering Cancer Center
Treatment
Cohort study
Type 1
Type 1 and 2
Pathologic type
Internal and external (Bendifallah et al. 2014)
Internal and external (Bendifallah et al. 2014)
0.82 (95 % CI 79–85)
External
Internal and external (Koskas et al. 2012; Poltauer et al. 2012)
Validation
CPHM Cox proportional hazards model, DFS disease-free survival, DR distant relapse, EBRT external-beam radiotherapy, FIGO International Federation of Gynecology and Obstetrics, LRR locoregional recurrence, LVSI lymphovascular space involvement, NS not specified, OS overall survival, P/PA LND pelvic and/or para-aortic lymph node dissection, VBT vaginal brachytherapy
2097
Nomogram CPHM
396
Kondalsamy et al. 201229
MLR
Risk scoring
1735
No. of FIGO women stage
Bendifallah et al. 201427
Abu-Rustum Nomogram CPHM et al. 201020
Study
TABLE 3 continued
982 S. Bendifallah et al.
Predictive Models in Endometrial Cancer
Recurrence and Survival Outcomes: Impact of Predictive Models A debate exists regarding the recurrence and survival outcomes in EC. Although EC is characterized by a good prognosis, many authors have underlined the high heterogeneity in early-stage EC, exposing patients to over- or undertreatment (Table 3).4,8,10,50,60 Recurrence after primary treatment of EC confined to the uterus is widely variable, ranging from 2 to 26 %.27,50,54 Kondalsamy-Chennakesavan et al. focused on the incidence of 3-year recurrence and aimed to differentiate between isolated locoregional recurrences (LRR) and distant recurrences (DR).29 The nomograms included the following covariates: age at diagnosis, FIGO stage (2009), tumor grade, LVSI, histologic type, depth of myometrial invasion, and peritoneal cytology. The nomograms achieved concordance indices (C index) of 0.73 and 0.86 for isolated LRR and DR, respectively. In 2014, using a multicenter database of 322 women, Bendifallah et al. assessed the external validity in patient with surgically treated early-stage endometrioid EC (Table 4).58 The C index was 0.65 (95 % confidence interval 0.61–0.69) for the LRR nomogram and 0.71 (95 % confidence interval 0.68–0.74) for the DR nomogram, resulting in a moderate calibration. Nevertheless, the observed heterogeneity in the management of patients with EC over the last 10 years in different countries may have affected the exportability of the model. Recently an RSS has been developed to stratify recurrence risk in patients with early-stage type 1 EC.27 The RSS was also externally validated using data from an independent population. The interest of this tool lies in identifying two subsets of patients with low and high risk of recurrence among patients with early-stage type 1 EC. The FIGO classification was developed to estimate overall oncologic outcomes.2 However, additional factors that are not included in the FIGO classification such as age, histologic tumor characteristics, and health status or comorbidities may play equally important roles in prognosis and overall outcome and recurrence.10,33,35,36 Three nomograms have been developed to predict OS with a view to helping clinicians offer better patient counseling and provide more individualized postoperative management. In 2010, Abu-Rustum et al. introduced the first tool to provide an individualized estimation of 3-year OS after primary therapy.20 The authors demonstrated that their nomogram was significantly more accurate than the FIGO staging risk grouping alone (area under the curve = 0.702) overcoming part of the heterogeneity of outcome prediction within each FIGO risk group. This predictive model was the first to be externally and positively validated.61 Similarly, Koskas et al., using 64,023 patients from the Surveillance,
983
Epidemiology, and End Results database, reported that the nomogram can accurately predict 3-year OS whether the patient underwent adjuvant radiotherapy or not.62 Indeed, among the whole population, predicted and observed 3-year OS were 85.2 and 85.6 % (±0.1 %), respectively. In patients with adjuvant radiotherapy, OS values were 81.0 and 83.1 % (±0.3 %), and in those without, they were 86.5 and 86.3 % (±0.2 %), respectively. The C indices for the whole population, in patients with and without radiotherapy, were 0.811 (±0.004), 0.751 (±0.009), and 0.803 (±0.006), respectively. Recently, AlHilli et al. published new tools to predict 3- and 5-year OS including a more specific population of 1281 patient with LR and HR EC from the Mayo Clinic.63 Estimated 5-year OS were 87.0 and 51.5 % for LR and HR, respectively. To date, neither of these nomograms has been externally validated. The latest contribution concerning survival assessment has been suggested by Creutzberg et al., who developed tools to predict LRR, DR, OS, and disease-free survival, with the aim of facilitating treatment decision support for individual patients.64 Clinical trial data from the randomized Post Operative Radiation Therapy for EC (PORTEC-1; n = 714 patients) and PORTEC-2 (n = 427 patients) trials and registered group (grade 3 and deep invasion, n = 99) were pooled for analysis (n = 1240). Two external validation sets (n = 244 and 291 patients) were used. The nomograms were partly able to predict long-term outcome for EC. The value of these tools is that they include patients with endometrioid adenocarcinoma who underwent primary surgery with observation, pelvic external-beam radiotherapy, or vaginal brachytherapy after surgery. The main limitations of all the current nomograms are that they take into account only classic clinicopathologic risk factors and that the predictive weight of each predictor seems to be in contradiction with current literature. Finally, Lakhman et al. developed a preoperative chemotherapy-based nomogram for predicting OS in patients with nonendometrioid carcinomas.65 However, the C index for the nomogram was poor (0.640 ± 0.028), which is a major limitation for its exportability. CONCLUSION AND PERSPECTIVES IN ENDOMETRIAL CANCER Although the past decade has witnessed the emergence of several remarkable clinical, pathologic, molecular, and imaging prognostic factors, there is room for improvement in individual patient risk assessment that should be integrated in future EC management models.32 Given the substantial genetic and morphologic heterogeneity in EC, individualized patient care may be improved by the integration of novel biomarkers or data derived from genomic analysis in current available models.8,60,67 Hence, further studies are needed to validate to what extent such
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
Nomogram CPHM
Nomogram CPHM
Algorithm
Koskas et al. 201162
Polterauer et al. 201261
Kang et al. 201359
MLR
FIGO 1988 stages I–IV
MLRM
Algorithm
Todo et al. 200726
FIGO stage
Statistical model
Predictive model
Study
Cohort study
Treatment
Type 1 and • Retrospective Total HT ± P/PA LND 2 • Multicenter • Women treated between 2000 and 2008
Preoperative prediction of LNM
3 y overall survival
Type 1 and • Prospective Primary surgical and 2 adjuvant therapy • Medical according to University of international Vienna and guidelines Innsbruck • Women treated between 1995 and 2011
3 y overall survival
Preoperative prediction of LNM
Outcome
Type 1 and • Retrospective Total HT ± P/PA 2 LND ± EBRT ± • U.S. National chemotherapy ± Cancer brachytherapy Institute database from 1988 and 2007
• Women treated between 2000 to April 2005
University and 12 affiliated hospitals
Type 1 et 2 • Retrospective Total HT ± P/PA LND • Hokkaido
Pathologic type
TABLE 4 Available external validation studies of predictive models
Todo et al. 2003
• CA-125 level
• Histologic grade
• Volume index
• Histologic type
Predictors
319
765
Kang et al. 2012
2009
Whole population 0.811 (±0.004)
NR
Concordance index
• 3 MRI parameters
• Serum CA125 levels
• Histologic type
• Final grade
• 1988 FIGO stage
NR
• Number of Good calibration negative lymph nodes
0.71 (95 % CI 0.68–0.74)
Good calibration • Histologic type
• Final grade
Women without radiotherapy 0.803 (±0.006)
• 1988 FIGO stage
• Number of Women with radiotherapy 0.751 negative lymph nodes (±0.009)
Abu-Rustum • Age at diagnosis et al.
2009
64,023 Abu-Rustum • Age at diagnosis et al.
216
No. of Model women validated
984 S. Bendifallah et al.
FIGO 2009 stages I–III
Nomogram CPHM
Bendifallah et al. 201458
Treatment
• Women treated between 2007 and 2012
• Retrospective Total HT ± P/PA LND • Multicenter
Cohort study
271
Predictors
• Grade
2012
• Myometrial invasion
• Histologic subtype
• LVSI
• 2009 FIGO stage
Kondalsamy et al.
• ±TD
• LVSI
• Cervical stromal invasion
• FIGO grade
AlHilli et al. • Myometrial 2012 invasion
No. of Model women validated
Defined as nodal 322 metastasis when P/ PA LND was performed or P/PA lymph node recurrence after negative LND or when LND was not performed
Outcome
Type 1 and • Retrospective Total HT ± P/PA 3 y isolated LND ± EBRT 2 locoregional and • Multicenter ± brachytherapy ± distant recurrence • Women chemotherapy ± treated hormone therapy between 2007 and 2012
Type 1
Pathologic type
0.69 (95 % CI 0.58–0.79) and 0.66 (95 % CI 0.60–0.71) for 3 y isolated locoregional and distant recurrence models, respectively
0.65 (95 %, 0.61– 0.69) for model with TD and 0.71 (95 %, 0.68– 0.74) for model without
Concordance index
CI confidence interval, CPHM Cox proportional hazards model, EBRT external-beam radiotherapy, FIGO International Federation of Gynecology and Obstetrics, HT total hysterectomy, LND lymph node dissection, LNM lymph node metastasis staging system, LVSI lymphovascular space involvement, MLR multiple linear regression, MLRM mammography logistic regression model, MRI magnetic resonance imaging, NR not reported, P/PA LND pelvic and/or para-aortic lymph node dissection, TD tumor diameter
FIGO 2009 stages I–IV
Nomogram MLR
FIGO stage
Bendifallah et al. 201366
Statistical model
Predictive model
Study
TABLE 4 continued
Predictive Models in Endometrial Cancer 985
986
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FIG. 2 State of Art in endometrial cancer management
biomarkers can be applied to improve risk stratification and to tailor surgical and adjuvant treatment. Although the predictive approach has been reported to be relevant in several fields of oncology (breast, bladder, and prostate cancer), none of the currently available predictive tools in EC is accurate enough in terms of clinical objectives, statistical accuracy, generalizability, or level of complexity.15,40,68 Several limitations should be underlined and improved. Finally, EC management is constantly progressing and improving. The emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches (Fig. 2). DISCLOSURE
The authors declare no conflict of interest.
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2. Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105;103– 4. 3. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63: 11–30. 4. Creasman WT, Odicino F, Maisonneuve P, et al. Carcinoma of the corpus uteri. FIGO 26th Annual Report on the Results of Treatment in Gynecological Cancer. Int J Gynaecol Obstet. 2006;95 Suppl 1:S105–43. 5. Kinkel K, Forstner R, Danza FM, et al. Staging of endometrial cancer with MRI: guidelines of the European Society of Urogenital Imaging. Eur Radiol. 2009;19:1565–74. 6. Haldorsen IS, Salvesen HB. Staging of endometrial carcinomas with MRI using traditional and novel MRI techniques. Clin Radiol. 2012;67:2–12. 7. Ballester M, Dubernard G, Le´curu F, et al. Detection rate and diagnostic accuracy of sentinel-node biopsy in early stage endometrial cancer: a prospective multicentre study (SENTIENDO). Lancet Oncol. 2011;12:469–76. 8. Salvesen HB, Haldorsen IS, Trovik J. Markers for individualised therapy in endometrial carcinoma. Lancet Oncol. 2012;13:e353– 61. 9. Morneau M, Foster W, Lalancette M, et al. Adjuvant treatment for endometrial cancer: literature review and recommendations
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987 30. Kattan MW. Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol. 2003;13:111–16. 31. Barlin JN, Soslow RA, Lutz M, et al. Redefining stage I endometrial cancer: incorporating histology, a binary grading system, myometrial invasion, and lymph node assessment. Int J Gynecol Cancer. 2013;23:1620–28. 32. Murali R, Soslow RA, Weigelt B. Classification of endometrial carcinoma: more than two types. Lancet Oncol. 2014;15:e268– 278. 33. Creutzberg CL, van Putten WL, Koper PC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. PORTEC Study Group. Post Operative Radiation Therapy in Endometrial Carcinoma. Lancet 2000;355:1404–11. 34. Keys HM, Roberts JA, Brunetto VL, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study. Gynecol Oncol. 2004;92:744–51. 35. Todo Y, Kato H, Kaneuchi M, et al. Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis. Lancet. 2010;375:1165–72. 36. Bendifallah S, Canlorbe G, Raimond E, et al. A clue towards improving the European Society of Medical Oncology risk group classification in apparent early stage endometrial cancer? Impact of lymphovascular space invasion. Br J Cancer. 2014; doi: 10. 1038/bjc.2014.237 37. Bendifallah S, Canlorbe G, Collinet P, et al. Just how accurate are the major risk stratification systems for early-stage endometrial cancer? Br. J. Cancer (2015). doi:10.1038/bjc.2015.35 38. Silverman MB, Roche PC, Kho RM, et al. Molecular and cytokinetic pretreatment risk assessment in endometrial carcinoma. Gynecol Oncol. 2000;77:1–7. 39. Cancer Genome Atlas Research Network, Kandoth C, Schultz N, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497:67–73. 40. Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26:1364–70. 41. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87. 42. Abbott RD. Logistic regression in survival analysis. Am J Epidemiol. 1985;121:465–71. 43. Hosmer DW, Hjort NL. Goodness-of-fit processes for logistic regression: simulation results. Stat Med. 2002;21:2723–38. 44. Steyerberg EW, Eijkemans MJ, Van Houwelingen JC, Lee KL, Habbema JD. Prognostic models based on literature and individual patient data in logistic regression analysis. Stat Med. 2000;19:141–60. 45. American College of Obstetricians and Gynecologists. ACOG practice bulletin, clinical management guidelines for obstetriciangynecologists, number 65, August 2005: management of endometrial cancer. Obstet Gynecol. 2005;106:413–25. 46. Ko EM, Funk MJ, Clark LH, Brewster WR. Did GOG99 and PORTEC1 change clinical practice in the United States? Gynecol Oncol. 2013;129:12–17. 47. ASTEC study group, Kitchener H, Swart AM, et al. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. Lancet. 2009;373:125–36. 48. Nout RA, Smit VT, Putter H, et al. Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. Lancet. 2010;375:816–23.
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REVIEW ARTICLE – GYNECOLOGIC ONCOLOGY
Predictive Modeling: A New Paradigm for Managing Endometrial Cancer Sofiane Bendifallah, MD1,2, Emile Daraı¨, MD, PhD1,3, and Marcos Ballester, MD, PhD1,3 1
Department of Gynecology and Obstetrics, Tenon University Hospital, Assistance Publique des Hoˆpitaux de Paris (APHP), University Pierre and Marie Curie, Institut Universitaire de Cance´rologie (IUC), Paris 6, France; 2INSERM UMR S 707, ‘‘Epidemiology, Information Systems, Modeling,’’, University Pierre and Marie Curie, Paris 6, France; 3INSERM UMR S 938, University Pierre et Marie Curie, Paris 6, France
ABSTRACT With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.
Endometrial Cancer (EC) is the most frequent gynecologic cancer in developed countries and ranks overall as the fourth most common cancer in woman (after breast, lung and bronchus, and colon and rectum).1 Three-quarters of patients are diagnosed at an early stage (International Federation of Gynecology and Obstetrics [FIGO] stage I or II disease), with a 5-year overall survival (OS) ranging Society of Surgical Oncology 2015 First Received: 17 September 2015; Published Online: 17 November 2015 S. Bendifallah, MD e-mail: [email protected]
from 74 to 91 %.2–4 In contrast, advanced disease stages are associated with a reported 5-year OS of 57–66 % for FIGO stage III and 20–26 % for FIGO stage IV disease.4 Over the past decade, new options in diagnostic and treatment modalities have created a shift in the medical decision process for EC.5–12 This has given rise to the emergence of individualized medicine, where each woman with EC can benefit from an individualized and customized treatment approach.8,13–15 Currently, the clinician needs to consider imaging techniques, surgical options, new adjuvant drugs, and the patient’s quality of life—as well as, likely in the near future, genomic data.5,6,8,10,11,16,17 Such a combination of factors renders clinical decision making difficult, even confusing at the individual level. To overcome such limitations, a new paradigm has emerged that is based on the implementation of predictive, personalized, preventive, and participatory models.15,18,19 Various predictive tools (algorithms, nomograms, and risk scoring systems [RSS]) have been developed.20–29 However, to our knowledge, no standardized assessment of their robustness, reproducibility, or clinical utility has been formally reported.15,19,30 In this review, we highlight existing models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. CURRENT CLINICAL CLASSIFICATIONS OF ENDOMETRIAL CANCER The most adopted classifications are the 2009 FIGO2 and the tumor, node, metastasis (TNM) classification systems, which are based on surgical staging and which stratifies cancer according to the local extent of the tumor (T), whether the cancer has spread to lymph node (LN) (N),
976
S. Bendifallah et al.
TABLE 1 Description of six classifications systems currently used in EC management Classification
Year of publication
No. of patients
Criteria included
TNM, FIGO2
2009
–
• Extent of tumor (T) • Spread to lymph nodes (N) • Spread to distant sites (M)
PORTEC-133
2000
715
• Age • Histologic type (endometrial adenocarcinoma) • Histologic grade (1, 2, 3)
GOG-9934
2004
382
• Myometrial invasion (C50 and \50 %) • Age • Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • FIGO stage
SEPAL35
2010
671
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • FIGO stage • LVSI
ESMO
10
2013
–
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • FIGO stage for myometrial invasion (C50 and \50 %)
ESMO modified36
2014
496
• Histologic type (endometrial adenocarcinoma, serous carcinoma or clear cell carcinoma of any stage) • Histologic grade (1, 2, 3) • Myometrial invasion (C50 and \50 %) • LVSI
ESMO European Society of Medical Oncology, FIGO International Federation of Gynecology and Obstetrics, GOG Gynecologic Oncology Group, LVSI lymphovascular space involvement, PORTEC Post Operative Radiation Therapy in Endometrial Carcinoma, SEPAL Survival Effect of Para-Aortic Lymphadenectomy in Endometrial Cancer, TNM tumor, node, metastasis classification system
and whether it has spread to distant sites (M).2,31,32 To overcome the existing classification limitations, prognostic factors have been aggregated and added to several RSS, namely the Post Operative Radiation Therapy in Endometrial Carcinoma (PORTEC) 1 classification, the Gynecologic Oncology Group (GOG) 99 classification, the Survival Effect of Para-Aortic Lymphadenectomy in Endometrial Cancer (SEPAL) classification, the European Society of Medical Oncology (ESMO) classification, and the ESMO modified classification—all of which are currently used worldwide to guide decision making and clinical trial design (Table 1).10,33–37 Although the core variables of these classifications are similar, it appears that for major classifications, most have never been externally validated, accuracy is not reported, and only one simultaneous comparison using the same cohort has been performed.10,32–36,38,39
METHODOLOGIC PROCESSES FOR MODELING PREDICTION TOOLS We provide a schematic overview of the methodologic processes for predictive model development, describing model development and assessing its clinical usefulness, in Fig. 1.40–44,68 ENDOMETRIAL CANCER CARE CHALLENGES Surgery plays a major role in the management of EC, especially in patients with early-stage disease. However, practice patterns vary widely according to national and international guidelines.9–12,35,45 This is mainly due to several criteria defining risk groups for recurrence, unstandardized protocols for surgical staging, and different indications for adjuvant therapies.10,33,34,46–48
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DATA SELECTION
FIRST STEP
(Litterature review, hypothesis, inclusion criteria, eligibility) Population of interest
Primary outcome
Predictors
-
Single institution
-
Binary (presence/absence)
-
Clinical evidence (review)
-
Multiple centers
-
Censored outcome (Time
-
Statistical significance
to event)
CONSTRUCTION OF THE MODEL (On training data set) Analysis and Testing predictors
Select the Model
-
-
Logistic regression
Statistical significance expressed as P value
model for binary
SECOND STEP
Threshold for clinical decision making
outcome -
-
prior publication
Cox proportional hazard model for
-
censored outcome
-
On clinical evidence
-
On statistical significance
Clinical significance according to
Predictors interactions and multicollinearity
Nomogram and Algorithm Graphical or a mathematical tools: the effect of predictors on the outcome of interest is represented either in the format of axes, and risk points are attributed or in mathematical formula Stratification Scoring System Risk grouping tools which place similar, though not identical, patients into the same group and attempt to predict an individual prognosis based on the behaviour of the group
THIRD STEP
VALIDATION of PREDICTIVE ACCURACY (External data set or internal statistical approach) Discrimination -
The area under the receiver operating characteristic curve indicates the predictive properties of a model and quantifies the overall accuracy
Generalizability -
General applicability for clinical practice
Level of complexity
Calibration -
Calibration plots show the relationship between predicted and observed probabilities of the outcome of interest. Clinical utility
FIG. 1 Schematic overview of methodological processes for predictive model development
Optimal Lymph Node Assessment: The Place of Predictive Models Selecting patients who might benefit from systematic complete nodal staging is a major issue to guide postoperative treatment in patients with early-stage EC.7,47,49,50 Indeed, LN status is one of the most important prognostic factors (Table 2).4,51–54
In this area of research, a meta-analysis of two randomized trials on the impact of systematic lymphadenectomy in early-stage EC showed no benefit on overall and recurrence-free survival.47,49,55 Despite the unestablished therapeutic role of lymphadenectomy, it remains the most reliable method to evaluate LN status, providing important prognostic information and assisting in the tailoring of adjuvant therapies. However, complete
Type of predictive model
Selecting women for secondary LND
Selecting women for secondary LND
Selecting women for secondary LND
Nomogram MLR
Nomogram MLR
Algorithm
AlHilli et al. 201321
Bendifallah et al.201223
Kamura et al. 199924
MLR
To determine extent of LND
FIGO 2009 stages I–IV
175
FIGO 1988 stages I–IV
18,294 FIGO 2009 stages I–III
883
397
Care objective No. of FIGO women stage
Nomogram MLR
Statistical model
Kang et al. 201459
Postoperative predictive model
Study
TABLE 2 Predictive models for lymph node status in EC
Type 1
• Women treated between 1979 and 1994
Hospital
• Retrospective Total HT, ±P/PA • Kyushu LND University
LNM
Type 1 and • Retrospective Total HT, LNM and P/ 2 • U.S. National PA LND Cancer Institute data base from 1988 and 2007
Type 1
Defined as nodal metastasis when P/PA LND was performed or P/PA lymph node recurrence after negative LND or when LND was not performed
Outcome
• Retrospective Total HT, ±P/PA • Mayo Clinic LND • Women treated between 1999 and 2008
Treatment
Para aortic metastasis
Cohort study
Type 1 and • Retrospective Total HT, 2 P/PA • Multicenter LND
Pathologic type
Internal
Validation
Internal validation, 0.80 (95 % CI 0.79– 0.81); external validation by Bendifallah et al.
Internal and external
0.88 and 0.87 Internal and for model external including (Bendifallah TD and et al. 2013) model without TD, respectively
0.87
Concordance index
Sensitivity, NA specificity, and accuracy were 83, 72, • Macroscopic and 73 %, tumor diameter respectively
• Microscopic degree of myometrial invasion
• Location of tumor
• Histologic subtype
• Grade
• Race
• Age
• LVSI • ±TD
• Cervical stromal invasion
• FIGO grade
• Myometrial invasion
• CA-125
• LVSI
• Pathologic type
• Myometrial invasion
Predictors
978 S. Bendifallah et al.
Type of predictive model
Statistical model
MLR
MLR
Lee et al. 201225 Algorithm
Algorithm
181
Selecting 214 women for LND with risk stratification
Preoperative 360 selection of women with low-Risk of LNM
Selecting 774 women with advanced disease
Selecting women for LND
Pathologic type
Cohort study
Treatment
Outcome
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
FIGO 2009 stages I–IV
• Women treated between 1993 and 2000
Total Preoperative Type 1 and • Multicenter HT ± P/ prediction 2 • Hokkaido and PA LND of LNM 2 affiliated hospitals
Type 1 and • Retrospective Total HT Preoperative and P/ prediction 2 • Multicenter PA LND of LNM • Women treated between 2002 and 2008
• Women treated between 2008 and 2012
Type 1 and • Retrospective Total HT Preoperative and P/ prediction 2 • Helsinki PA LND of LNM University Central Hospital
Presumed Type 1 and • Retrospective Total HT Preoperative FIGO and P/ prediction 2 • Multicenter 2009 IPA LND of LNM population II • Women treated between 2000 and 2010
Care objective No. of FIGO women stage
Validation
• Serum CA125 level
• Histologic grade
• Volume index
Internal and external by Todo et al. (2007)
NA
• 3 MRI parameters • Histologic type
Internal
0.85 (95 % CI Internal and 0.79–0.92) external by Kang et al. (2013)
0.823
0.77 (95 % CI Internal 0.70–0.83)
Concordance index
• Serum CA125 levels
• High-risk histology
• Elevated serum CA-125
• Thrombocytosis
• Leukocytosis
• Primary site tumor
• Histologic subtype
• Grade
• Race
• Age
Predictors
CI confidence interval, FIGO International Federation of Gynecology and Obstetrics, LND lymph node dissection, LNM lymph node metastasis staging system, LVSI lymphovascular space involvement, MLR multiple linear regression, NA not applicable, HT total hysterectomy, P/PA LND pelvic and/or para-aortic lymph node dissection, TD tumor diameter
Todo et al. 200726
MLR
Algorithm
Luomarantaet al. 201356
Preoperative predictive model Koskas et al. Algorithm MLR 201457
Study
TABLE 2 continued
Predictive Models in Endometrial Cancer 979
980
pelvic and/or para-aortic lymphadenectomy surgery is associated with an increased risk of morbidity, such as lymphocyst or lymphoedema.47,49 Several authors have reported preoperative predictive models of LN status, such as algorithms or nomograms, as a more reliable way of promoting a personalized therapeutic strategy that may help decide the extent of surgical staging.25,26,56,57 In 2013, Luomaranta et al. proposed a preoperative algorithm to predict the probability of LN and distant metastasis in patients with EC undergoing systematic pelvic and/or para-aortic lymphadenectomy.56 The final score system was obtained by combining leukocytosis, thrombocytosis, CA-125 values, and High Risk (HR) histology. However, this predictive model has not been externally validated since its publication. Moreover, there is some concern about the way in which the parameters were assigned. Similarly, Koskas et al. also proposed an algorithm focusing on patient with early-stage EC for whom indication for lymphadenectomy is still a matter of debate.57 The authors combined preoperative tumor characteristics provided by endometrial sampling (i.e., histologic subtype and grade) and magnetic resonance imaging information (depth of myometrial invasion and extension to cervical stroma) to predict LN status. This algorithm showed good discrimination and was well calibrated. Both these predictive models represent a valuable contribution that may improve preoperative EC management. Moreover, they are limited by a lack of external validation and a considerable level of complexity. Lee et al. developed a preoperative model to identify a low risk (LR) group before surgery for whom nodal staging should not be performed.25 However, although it has been shown to be exportable, the major concern of this model is its clinical relevance. This is because the most relevant question for early-stage EC is not determining who should be spared pelvic and/or para-aortic lymphadenectomy but rather which patient has an increased risk of LN metastasis. Moreover, there is seemingly little correlation between LN diameter and the presence of LN metastases.10 Similarly, Todo et al. described a preoperative model based on tumor volume measured by magnetic resonance imaging, serum CA-125 level, histologic type, and grade.26 In contrast with previous models, those two predictive models were externally validated with a demonstrated statistical accuracy. Although the Todo algorithm improves patient selection, there is some concern about the robustness of the external validation—both patient populations were of Asian origin—raising the issue of its applicability in European or American populations. Furthermore, the fairly complex nature of the model renders it difficult to use in routine practice. Finally, the rate of LN metastases corresponds to the metastasis rate reported in risk groups of the current classifications, which limits the interest of the model.37 The model based on final histology
S. Bendifallah et al.
was designed by Kamura et al. from a cohort of 175 patients with EC from the Kyushu University Hospital who were treated by hysterectomy combined with pelvic LN dissection.24 The major interest of such a formula lies in the evaluation of a cutoff point and the ability to assess these parameters intraoperatively. However, this predictive model has not yet been externally validated. In 2012, Bendifallah et al. suggested a nomogram based on pathologic hysterectomy characteristics to provide a more individualized estimation of LN metastasis in stage FIGO I–IIIc type 1 and 2 EC.23 Although, this nomogram is based on a solid methodology with the use of internal and external validation sets, multicenter data, and clinically relevant variables, it does not take into account the subset of type 1 EC; it lacks a few major predictive factors, such as the lymphovascular space involvement (LVSI) and tumor diameter; and there is no optimal decision threshold. As an alternative, AlHilli et al. developed a nomogram for the prediction of lymphatic dissemination after hysterectomy focusing on type 1 EC.22 Two nomograms were designed (a full and an alternative model) with and without the tumor diameter as a predictive factor associated with classic predictors. Myometrial invasion, tumor diameter, FIGO grade, cervical stromal invasion, and LVSI were included in the nomogram. Although this nomogram is relevant because of the specific patient population and the inclusion of major predictive factors such as LVSI and tumor diameter, the external validation study provided by Bendifallah et al. in 2013 showed that the tools were only partly generalizable to a new and independent patient population.58 Therefore, although these tools provide a more individualized estimation of LN dissemination, additional parameters are needed to allow higher accuracy for counselling patient in clinical practice. Finally, Kang et al. developed a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with EC.59 The tool was designed to determine the extent of lymphadenectomy after incomplete surgery but no external validation study was performed. More recently, Bendifallah et al. designed an RSS for predicting LN metastases in patients with earlystage EC.28 LN metastases were associated with five variables: age C60 years, histologic grade 3 and/or type 2 disease, primary tumor diameter C1.5 cm, depth of myometrial invasion C50 %, and positive LVSI status. A total score of 6 points corresponded to the optimal threshold of the RSS, with a rate of LN metastases of 7.5 and 34.7 % for LR (B6 points) and HR patients ([6 points), respectively. At this threshold, the diagnostic accuracy was 83 %. This RSS could be useful in clinical practice to determine which patients with early-stage EC should benefit from secondary surgical staging including complete lymphadenectomy.
1240
193
925
356
Nomogram CPHM
Nomogram CPHM
Lakhman et al. 201465
AlHilli et al. Nomogram CPHM 201463
AlHilli et al. Nomogram CPHM 201463
FIGO 2009 stages I–IV
FIGO 2009 stages I–IV
FIGO 2009 stages I–IV
Early stages
No. of FIGO women stage
Creutzberg et al. 201564
Statistical model
Predictive model
Study
3 and 5 y OS • Age
High risk: grade 3, and nonendometrioid EC • Mayo Clinic
Total HT ± P/PA LND ± EBRT ± chemotherapy • Women treated ± brachytherapy between 1999 and 2008
• Retrospective
• Retrospective Low-risk: FIGO Total HT ± P/PA grades 1 and 2 LND ± EBRT ± • Mayo Clinic endometrioid EC chemotherapy • Women treated ± brachytherapy between 1999 and 2008
Total HT ± P/PA LND ± • Memorial chemotherapy Sloan-Kettering ± radiation Cancer Center
• Para aortic nodal status
• Adjuvant therapy
• Cervical stromal invasion
• LVSI
• FIGO 2009 stage
• ASA
3 and 5 y OS • Age
• Pelvic node status
• Postoperative complication
• Primary TD
• FIG0 2009 stage
• Cardiovascular disease
• Pulmonary dysfunction
3 and 5 y OS • Age
• Omental implants
• Ascites
• Myometrial invasion
• LVSI
• Grade
• 2009 FIGO stage
• Retrospective
0.759
0.803
0.64
Internal
Internal
Internal
DR (0.73), DFS External (0.69), OS (0.70), LRR (0.59)
• Age
Validation
Concordance index
Predictors
Type 2
Outcome
LRR, DR, OS, and DFS
Treatment
• Pooled cohort Total HT ± P/PA LND ± none ± from PORTECEBRT ± VBT 1 and PORTEC-2
Cohort study
Type 1
Pathologic type
TABLE 3 Available predictive models for recurrence and survival in EC
Predictive Models in Endometrial Cancer 981
Predictive model
Statistical model
2097
Nomogram CPHM
Kondalsamy et al. 201229
FIGO 2009 stages I–III
FIGO 2009 stages I–III
FIGO 2009 stages I–III
FIGO 1988 stages I–IV Not specified
• Prospective
3 y distant • Age recurrence • 2009 FIGO stage
Primary surgery (NS) ± • Multicenter EBRT ± • Women treated brachytherapy ± between 1997 chemotherapy ± and 2007 hormone therapy
• Retrospective
Type 1 and 2
Primary surgery ± EBRT ± brachytherapy ± • Women treated chemotherapy ± between 1997 hormone therapy and 2007
3 y isolated LRR
• Retrospective
• Peritoneal washing
• Histologic type
• LVSI
• Grade
• Myometrial invasion
• Histologic type
• LVSI
• Grade
• 2009 FIGO stage
• Age
• LVSI
• Grade
• Tumor diameter
3y • Age recurrence • Myometrial invasion
• Histologic subtype
• Final grade
• 1988 FIGO Stage
• Number of negative lymph nodes
Type 1 and 2
• Multicenter
Concordance index
0.86
0.73
0.74 (95 % CI 0.71–0.77)
• Age at diagnosis 0.746 ± 0.011
Predictors
Total HT ± P/PA LND ± EBRT ± • Multicenter chemotherapy ± • Women treated brachytherapy between 2001 and 2012
3 y overall survival
Outcome
• Retrospective
• Women treated between 1988 and 2002
• Memorial Sloan-Kettering Cancer Center
Treatment
Cohort study
Type 1
Type 1 and 2
Pathologic type
Internal and external (Bendifallah et al. 2014)
Internal and external (Bendifallah et al. 2014)
0.82 (95 % CI 79–85)
External
Internal and external (Koskas et al. 2012; Poltauer et al. 2012)
Validation
CPHM Cox proportional hazards model, DFS disease-free survival, DR distant relapse, EBRT external-beam radiotherapy, FIGO International Federation of Gynecology and Obstetrics, LRR locoregional recurrence, LVSI lymphovascular space involvement, NS not specified, OS overall survival, P/PA LND pelvic and/or para-aortic lymph node dissection, VBT vaginal brachytherapy
2097
Nomogram CPHM
396
Kondalsamy et al. 201229
MLR
Risk scoring
1735
No. of FIGO women stage
Bendifallah et al. 201427
Abu-Rustum Nomogram CPHM et al. 201020
Study
TABLE 3 continued
982 S. Bendifallah et al.
Predictive Models in Endometrial Cancer
Recurrence and Survival Outcomes: Impact of Predictive Models A debate exists regarding the recurrence and survival outcomes in EC. Although EC is characterized by a good prognosis, many authors have underlined the high heterogeneity in early-stage EC, exposing patients to over- or undertreatment (Table 3).4,8,10,50,60 Recurrence after primary treatment of EC confined to the uterus is widely variable, ranging from 2 to 26 %.27,50,54 Kondalsamy-Chennakesavan et al. focused on the incidence of 3-year recurrence and aimed to differentiate between isolated locoregional recurrences (LRR) and distant recurrences (DR).29 The nomograms included the following covariates: age at diagnosis, FIGO stage (2009), tumor grade, LVSI, histologic type, depth of myometrial invasion, and peritoneal cytology. The nomograms achieved concordance indices (C index) of 0.73 and 0.86 for isolated LRR and DR, respectively. In 2014, using a multicenter database of 322 women, Bendifallah et al. assessed the external validity in patient with surgically treated early-stage endometrioid EC (Table 4).58 The C index was 0.65 (95 % confidence interval 0.61–0.69) for the LRR nomogram and 0.71 (95 % confidence interval 0.68–0.74) for the DR nomogram, resulting in a moderate calibration. Nevertheless, the observed heterogeneity in the management of patients with EC over the last 10 years in different countries may have affected the exportability of the model. Recently an RSS has been developed to stratify recurrence risk in patients with early-stage type 1 EC.27 The RSS was also externally validated using data from an independent population. The interest of this tool lies in identifying two subsets of patients with low and high risk of recurrence among patients with early-stage type 1 EC. The FIGO classification was developed to estimate overall oncologic outcomes.2 However, additional factors that are not included in the FIGO classification such as age, histologic tumor characteristics, and health status or comorbidities may play equally important roles in prognosis and overall outcome and recurrence.10,33,35,36 Three nomograms have been developed to predict OS with a view to helping clinicians offer better patient counseling and provide more individualized postoperative management. In 2010, Abu-Rustum et al. introduced the first tool to provide an individualized estimation of 3-year OS after primary therapy.20 The authors demonstrated that their nomogram was significantly more accurate than the FIGO staging risk grouping alone (area under the curve = 0.702) overcoming part of the heterogeneity of outcome prediction within each FIGO risk group. This predictive model was the first to be externally and positively validated.61 Similarly, Koskas et al., using 64,023 patients from the Surveillance,
983
Epidemiology, and End Results database, reported that the nomogram can accurately predict 3-year OS whether the patient underwent adjuvant radiotherapy or not.62 Indeed, among the whole population, predicted and observed 3-year OS were 85.2 and 85.6 % (±0.1 %), respectively. In patients with adjuvant radiotherapy, OS values were 81.0 and 83.1 % (±0.3 %), and in those without, they were 86.5 and 86.3 % (±0.2 %), respectively. The C indices for the whole population, in patients with and without radiotherapy, were 0.811 (±0.004), 0.751 (±0.009), and 0.803 (±0.006), respectively. Recently, AlHilli et al. published new tools to predict 3- and 5-year OS including a more specific population of 1281 patient with LR and HR EC from the Mayo Clinic.63 Estimated 5-year OS were 87.0 and 51.5 % for LR and HR, respectively. To date, neither of these nomograms has been externally validated. The latest contribution concerning survival assessment has been suggested by Creutzberg et al., who developed tools to predict LRR, DR, OS, and disease-free survival, with the aim of facilitating treatment decision support for individual patients.64 Clinical trial data from the randomized Post Operative Radiation Therapy for EC (PORTEC-1; n = 714 patients) and PORTEC-2 (n = 427 patients) trials and registered group (grade 3 and deep invasion, n = 99) were pooled for analysis (n = 1240). Two external validation sets (n = 244 and 291 patients) were used. The nomograms were partly able to predict long-term outcome for EC. The value of these tools is that they include patients with endometrioid adenocarcinoma who underwent primary surgery with observation, pelvic external-beam radiotherapy, or vaginal brachytherapy after surgery. The main limitations of all the current nomograms are that they take into account only classic clinicopathologic risk factors and that the predictive weight of each predictor seems to be in contradiction with current literature. Finally, Lakhman et al. developed a preoperative chemotherapy-based nomogram for predicting OS in patients with nonendometrioid carcinomas.65 However, the C index for the nomogram was poor (0.640 ± 0.028), which is a major limitation for its exportability. CONCLUSION AND PERSPECTIVES IN ENDOMETRIAL CANCER Although the past decade has witnessed the emergence of several remarkable clinical, pathologic, molecular, and imaging prognostic factors, there is room for improvement in individual patient risk assessment that should be integrated in future EC management models.32 Given the substantial genetic and morphologic heterogeneity in EC, individualized patient care may be improved by the integration of novel biomarkers or data derived from genomic analysis in current available models.8,60,67 Hence, further studies are needed to validate to what extent such
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
FIGO 1988 stages I–IV
Nomogram CPHM
Nomogram CPHM
Algorithm
Koskas et al. 201162
Polterauer et al. 201261
Kang et al. 201359
MLR
FIGO 1988 stages I–IV
MLRM
Algorithm
Todo et al. 200726
FIGO stage
Statistical model
Predictive model
Study
Cohort study
Treatment
Type 1 and • Retrospective Total HT ± P/PA LND 2 • Multicenter • Women treated between 2000 and 2008
Preoperative prediction of LNM
3 y overall survival
Type 1 and • Prospective Primary surgical and 2 adjuvant therapy • Medical according to University of international Vienna and guidelines Innsbruck • Women treated between 1995 and 2011
3 y overall survival
Preoperative prediction of LNM
Outcome
Type 1 and • Retrospective Total HT ± P/PA 2 LND ± EBRT ± • U.S. National chemotherapy ± Cancer brachytherapy Institute database from 1988 and 2007
• Women treated between 2000 to April 2005
University and 12 affiliated hospitals
Type 1 et 2 • Retrospective Total HT ± P/PA LND • Hokkaido
Pathologic type
TABLE 4 Available external validation studies of predictive models
Todo et al. 2003
• CA-125 level
• Histologic grade
• Volume index
• Histologic type
Predictors
319
765
Kang et al. 2012
2009
Whole population 0.811 (±0.004)
NR
Concordance index
• 3 MRI parameters
• Serum CA125 levels
• Histologic type
• Final grade
• 1988 FIGO stage
NR
• Number of Good calibration negative lymph nodes
0.71 (95 % CI 0.68–0.74)
Good calibration • Histologic type
• Final grade
Women without radiotherapy 0.803 (±0.006)
• 1988 FIGO stage
• Number of Women with radiotherapy 0.751 negative lymph nodes (±0.009)
Abu-Rustum • Age at diagnosis et al.
2009
64,023 Abu-Rustum • Age at diagnosis et al.
216
No. of Model women validated
984 S. Bendifallah et al.
FIGO 2009 stages I–III
Nomogram CPHM
Bendifallah et al. 201458
Treatment
• Women treated between 2007 and 2012
• Retrospective Total HT ± P/PA LND • Multicenter
Cohort study
271
Predictors
• Grade
2012
• Myometrial invasion
• Histologic subtype
• LVSI
• 2009 FIGO stage
Kondalsamy et al.
• ±TD
• LVSI
• Cervical stromal invasion
• FIGO grade
AlHilli et al. • Myometrial 2012 invasion
No. of Model women validated
Defined as nodal 322 metastasis when P/ PA LND was performed or P/PA lymph node recurrence after negative LND or when LND was not performed
Outcome
Type 1 and • Retrospective Total HT ± P/PA 3 y isolated LND ± EBRT 2 locoregional and • Multicenter ± brachytherapy ± distant recurrence • Women chemotherapy ± treated hormone therapy between 2007 and 2012
Type 1
Pathologic type
0.69 (95 % CI 0.58–0.79) and 0.66 (95 % CI 0.60–0.71) for 3 y isolated locoregional and distant recurrence models, respectively
0.65 (95 %, 0.61– 0.69) for model with TD and 0.71 (95 %, 0.68– 0.74) for model without
Concordance index
CI confidence interval, CPHM Cox proportional hazards model, EBRT external-beam radiotherapy, FIGO International Federation of Gynecology and Obstetrics, HT total hysterectomy, LND lymph node dissection, LNM lymph node metastasis staging system, LVSI lymphovascular space involvement, MLR multiple linear regression, MLRM mammography logistic regression model, MRI magnetic resonance imaging, NR not reported, P/PA LND pelvic and/or para-aortic lymph node dissection, TD tumor diameter
FIGO 2009 stages I–IV
Nomogram MLR
FIGO stage
Bendifallah et al. 201366
Statistical model
Predictive model
Study
TABLE 4 continued
Predictive Models in Endometrial Cancer 985
986
S. Bendifallah et al.
FIG. 2 State of Art in endometrial cancer management
biomarkers can be applied to improve risk stratification and to tailor surgical and adjuvant treatment. Although the predictive approach has been reported to be relevant in several fields of oncology (breast, bladder, and prostate cancer), none of the currently available predictive tools in EC is accurate enough in terms of clinical objectives, statistical accuracy, generalizability, or level of complexity.15,40,68 Several limitations should be underlined and improved. Finally, EC management is constantly progressing and improving. The emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches (Fig. 2). DISCLOSURE
The authors declare no conflict of interest.
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