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Acta Radiol OnlineFirst, published on May 26, 2015 as doi:10.1177/0284185115587734
Original Article
Squamous cell carcinoma of the oral cavity and oropharynx: what does the apparent diffusion coefficient tell us about its histology?
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Luke Bonello1, Lorenzo Preda2, Giorgio Conte1, Caterina Giannitto1, Sara Raimondi3, Mohssen Ansarin4, Fausto Maffini5, Paul Summers2 and Massimo Bellomi1,2
Abstract Background: Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about microstructural tumor characteristics. Purpose: To prospectively analyze the correlation between the apparent diffusion coefficient (ADC) and clinicalhistologic characteristics of squamous cell carcinoma (SCCA) of the oral cavity and oropharynx. Material and Methods: Sixty-seven patients with untreated, histologically proven SCCA of the oral cavity and oropharynx underwent conventional and diffusion-weighted (b-values 0, 50, 250, 500, and 900 s/mm2) MRI. Tumor ADC was calculated from regions of interest drawn manually on the highest b-value images using ImageJ (ImageJ, NIH) and fsl (fsl 4, University of Oxford) image processing packages. ADC was calculated in two ways: standard ADC using all b-values; and ADCHigh using only b-values 250 s/mm2. We assessed the correlations between both ADC and ADCHigh and the clinical-histological characteristics of SCCA. Results: Fifty-two patients (36 men, 16 women; mean age, 55 13 years) were suitable for ADC calculation. Mean ADC was 1136.0 108.5 10–6 mm2/s. Mean tumor ADCHigh was 991.2 152.1 10–6 mm2/s. Mean tumor size was 32.3 13.4 mm (range, 14.0–69.0 mm). We observed no correlation of either ADC or ADCHigh values with any of the clinical-histological tumor characteristics. Undifferentiated tumors (G3) showed lower apparent diffusion coefficient values compared to differentiated ones (G1-G2), without reaching statistical significance. Conclusion: We did not observe any statistically significant correlation between ADC values and clinical-histological characteristics of SCCA of the oral cavity and oropharynx.
Keywords Diffusion-weighted imaging, apparent diffusion coefficient, squamous cell carcinoma, oral cavity, oropharynx, grading Date received: 26 January 2015; accepted: 28 April 2015
Introduction Cancers of the oral cavity and oropharynx are the most common head and neck tumors in the United States (1). More than 90% of them arise from the mucosal lining and are classified as squamous cell carcinomas (SCCA) (2). As most patients with SCCA of the oral cavity and oropharynx already have a clinical and histologic diagnosis when they present for radiologic examinations, the role of cross-sectional imaging is largely to provide information about the local invasion of the tumor into the surrounding structures as well as the regional
1
Specialisation School of Radiology, University of Milan, Milan, Italy Department of Radiology, European Institute of Oncology, Milan, Italy 3 Department of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy 4 Department of Head and Neck Surgery, European Institute of Oncology, Milan, Italy 5 Department of Pathology, European Institute of Oncology, Milan, Italy 2
Corresponding author: L Bonello, Division of Radiology, European Institute of Oncology, Via Ripamonti 435, Milan 20141, Italy. Email: [email protected]
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spread of the disease, as both have an impact on treatment and prognosis (1). The presence of nodal metastases is the most significant predictor of adverse outcome in head and neck SCCA (3). In the pre-treatment evaluation, the biological activity of SCCA is categorized by tumor grading, a histopathologic parameter correlated with tumor aggressiveness and the risk of nodal and distant metastases (4). Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about tumoral microstructural characteristics without the use of ionizing radiation or exogenous contrast-agents. DW-MRI explores the random (Brownian) motion of water molecules in biological tissues, and allows the calculation of the apparent diffusion coefficient (ADC), with ADC values varying according to the microstructure and physiological state of tissues (5). DW-MRI has shown encouraging results for diagnosis, risk stratification, and therapy monitoring in several body tumors (6), with reports that measurement of ADC may be used for the characterization of primary head and neck lesions (5) as well as for the discrimination of metastatic from normal lymph nodes (7). DW-MRI has also shown good potential in detecting early response to therapy (8) as well as differentiating persistent or recurrent head and neck SCCA from non-tumoral tissue changes after (chemo-) radiotherapy (9). A recent study by Hatakenaka et al. (10) gave preliminary evidence that pre-treatment ADC, along with T stage, is a potential predictor of local failure in head and neck SCCA treated with radiotherapy, alone or combined with chemotherapy. The aim of our study was to prospectively analyze the correlation between the apparent diffusion coefficient obtained with DW-MRI with the clinical and histologic characteristics of SCCA of the oral cavity and oropharynx.
Material and Methods Patients Our Institutional Ethics committee approved this study, and written informed consent was obtained from each patient prior to enrolment. Between June 2007 and November 2011, we prospectively enrolled 67 consecutive patients with newly diagnosed, histologically proven, SCCA of the oral cavity and oropharynx, who presented to our department for a staging MR examination. From the MR examinations of these patients, ADC calculation was not feasible in nine patients due to the presence of artifacts caused by metallic dental-work. In an additional six patients ADC calculation was not
possible as the tumor was not visible on the high b-value images despite being visible on conventional T1-weighted (T1W) or T2-weighted (T2W) images, thus precluding the drawing of regions of interest (ROIs). The remaining 52 patients (36 men, 16 women; mean age, 55 13 years) were included in the study.
Histology The final diagnosis for all patients was based on pathological findings obtained from the surgical specimens. Each specimen was fixed in buffered formalin for 24 h, paraffin embedded, cut to 5 micron thick sections and stained with hematoxylin and eosin. For every specimen, the histotype of the neoplasm, the anatomical structures involved, and grading were evaluated. The histotype of each tumor was defined according to the WHO classification of tumors (11). Tumor staging was performed according to the revised TNM staging system VII edition (12). Tumor grading was performed according to Broder’s classification; G1 when the undifferentiated component was less than 25% of the neoplasm, G2 when the undifferentiated component was less than 50% of the neoplasm, and G3 when the undifferentiated component was less than 75% (13).
MR technique All exams were performed on a 1.5 T MR scanner (Avanto, Siemens Medical Systems, Erlangen, Germany). Patients were examined in the supine position using a phased array coil. Based on a survey scan, the following MR images were acquired: axial T2W turbo spin echo (TSE) with radial trajectory filling of k-space (TR/TE, 6500/109 ms; slice thickness/gap, 3/0.6 mm; matrix, 320 320; acquisition time, 8 min 14 s) volume acquisition ranging from frontal sinus to pulmonary apices; axial T1W TSE (TR/TE, 500/14 ms; slice thickness/gap, 3.0/0.6 mm; matrix, 320 240; acquisition time, 6 min 8 s) with coverage to the T2W sequences; coronal and/or sagittal T2W TSE with radial trajectory filling of k-space (TR/TE, 3900/ 150 ms; slice thickness/gap, 3/0.6 mm; matrix, 320 320; acquisition time, 4 min 53 s) with range aimed on the volume of interest; diffusion weighted images (TR/TE, 5000/77 ms; slice thickness/gap, 5/1 mm; matrix, 160 130; acquisition time, 4 min 35 s) with 5 b-values (0, 50, 250, 500, 900 s/mm2) and fat suppression using a SPAIR technique (Spectral selection Attenuated Inversion Recovery); and an ultra-fast 3D T1W spoiled gradient echo scan with isotropic voxel dimensions following administration of contrast agent (TR/TE, 7.43/2.88 ms; flip angle, 12 ; matrix, 384 276; slice thickness/gap, 0.6/0.03 mm;
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acquisition time, 3 min 36 s). The T1 post-contrast sequence was utilized to generate 2 mm thick multiplanar reconstructions in the axial, coronal, and sagittal planes. All the sequences except the T1W post-contrast sequence utilized parallel imaging with the GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) algorithm with an acceleration factor of 2. The contrast agent used was gadopentate dimeglumine (Magnevist; Bayer Schering Pharma AG, Berlin, Germany) 0.2 mmol/kg and was administered at a flow rate of 2 mL/s, followed by a 20 mL bolus of saline solution at a flow rate of 2 mL/s.
Image and data analysis All examinations were anonymized and transferred to a workstation for analysis. A radiologist with 2 years of experience in head and neck MRI utilized all sequences available to identify the tumor. Tumor size was measured as the largest transverse diameter on the axial T1 post-contrast images.
The diffusion-weighted sequences and corresponding ADC maps were further transferred to a personal computer for evaluation of ADC values. Besides the standard calculation of the ADC map using all b-values (0, 50, 250, 500, 900 s/mm2) a map was calculated using only b-values of 250, 500, and 900 s/mm2 (ADCHigh). Regions of interest were carefully defined (Image J, NIH) to isolate the tumor from the surrounding tissues on all sections in which it was present based on the highest diffusion weighting (b-value ¼ 900 s/ mm2) images. The volumes obtained in this way were saved and used to extract mean, median, and standard deviation of the ADC values from the two maps for each tumor by scripts developed in-house based on the FMRIB Software Library (14) (Fig. 1).
Statistical analysis Baseline characteristics of patients and tumors were expressed as mean and standard deviation for continuous variables and as frequency and percentage for
Fig. 1. Illustration showing calculation and extraction of tumoral ADC values. The post-contrast axial T1W, fat-suppressed GE image (a) and axial T2W image (b) show a large tumor arising from the right side of the tongue extending past the midline. The diffusionweighted sequence with various b-values (b ¼ 0, 50, 250, 500, and 900 s/mm2; c–g, respectively) show a hyperintense signal within the tumor compared to the surrounding tissues. All images were exported in DICOM format to ImageJ, where ROI’s were manually drawn by the radiologist around the tumor margins on the high b-value (900 s/mm2) for every slice (h) in which the tumor was visible using the T1W and T2W images for reference. For each DWI scan, voxel-wise calculations of ADC (dashed line) and ADCHigh (dotted line) were respectively made using all five b-values (solid circles) and just the b-values of 250 and higher (Xs) as the latter may more accurately reflect the behavior of a slow diffusing ‘‘tissue water’’ compartment in a biexponential model (solid line) of in vivo water motion (i). The ADC and ADCHigh values calculated were then utilized to create maps for ADC and ADCHigh (j, k). The ADC and ADCHigh values within the set of ROIs, as illustrated by the respective histograms (l, m) were extracted and used to calculate mean, median, and standard deviation values for the tumor.
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categorical variables. The normality of the ADC and ADCHigh values distribution was verified by the Kolmogorov-Smirnov test. The association between ADC and ADCHigh values and histological characteristics of the tumors was assessed by T-tests for analyses with two groups, and one-way ANOVA for analyses with more than two groups. Since there were only two well differentiated tumors in our series, G1 and G2 lesions were grouped together for the statistical analysis. The correlation of ADC and ADCHigh values with patients’ age and tumor size was assessed using the Pearson correlation coefficient. P values < 0.05 were considered statistically significant. The analysis was performed with SAS (Statistical Analysis System) Software (SAS Institute Inc., Cary, NC, USA) version 9.2.
Results Tumor characteristics Out of the 52 tumors analyzed, 43 were located in the oral cavity and nine in the oropharynx. Mean tumor size was 32.3 13.4 mm (range, 14.0–69.0 mm). The histological characteristics regarding T, N status, and TNM stage are summarized in Table 1.
Table 1. Baseline characteristics of the study population. Tumors characteristics (n ¼ 52)
Mean (SD)
Size (mm)
32.3 (13.4) Tumors, n (%)
Grading 1 2 3 N 0 1 2 3 T 1 2 3 4 TNM stage I II III IV
2 (4%) 22 (42%) 28 (54%) 18 9 23 1
(35%) (18%) (45%) (2%)
1 9 1 41
(2%) (17%) (2%) (79%)
1 5 3 42
(2%) (10%) (6%) (82%)
Correlation between ADC and clinical-histologic tumor characteristics The mean tumor ADC value was: 1136.0 108.5 106 mm2/s. The mean tumor ADCHigh value was 991.2 152.1 10–6 mm2/s. There was no significant
Table 2. Association between ADC and histological characteristics. Histological characteristics Grading 1/2 3 N 0 1 2/3 N (0/1 vs. 2/3) 0/1 T 1/2 3/4 TNM stage I/II/III IV
ADC mean (SD)
P value 0.10
1162.5 (104.6) 1113.3 (108.4) 0.23 1133.1 (134.8) 1186.1 (100.7) 1113.7 (82.4) 0.21 1150.8 (125.1) 0.16 1179.4 (117.5) 1125.7 (105.1) 0.90 1139.3 (161.8) 1132.0 (95.1)
Student t-test for analysis with two groups and one-way ANOVA for the analysis with more than two groups.
Table 3. Association between ADCHigh and histological characteristics. Histological characteristics Grading 1/2 3 N 0 1 2/3 N (0/1 vs. 2/3) 0/1 T 1/2 3/4 TNM stage I/II/III IV
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ADCHighmean (SD)
P value 0.82
985.9 (162.4) 995.7 (145.6) 0.92 1006.2 (113.4) 981.2 (218.1) 998.5 (139.3) 0.99 997.9 (152.3) 0.28 944.0 (161.8) 1002.4 (149.5) 0.31 953.3 (128.2) 1007.8 (147.8)
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Fig. 2. A 62-year-old woman with a moderately differentiated (G2) SCCA of the retromolartrigone. The axial T2W MR image (a) shows a hyperintense mass lesion (arrows) in the retromolartrigone. On the gadolinium-enhanced T1W image (b), the mass is seen to infiltrate the right sub-lingual space, the medial pterygoid, and masseter muscles, as well as the mandibular ramus. The tumor is hyperintense on the b 900 diffusion-weighted image (c) with a mean ADC value of 1245.35 10–6 mm2/s (d), above the mean ADC of our cohort.
correlation of ADC and ADCHigh with patient age (P ¼ 0.43 and 0.76, respectively). No significant associations between ADC, ADCHigh, and histological characteristics of the tumors were observed (Tables 2 and 3). Since there were only two well differentiated tumors in our series, G1 and G2 lesions were grouped together for the statistical analysis. The mean ADC value of G1 þ G2 tumors was 1162.5 104.6 10–6 mm2/s (Fig. 2) whereas the mean ADC of G3 tumors was 1113.3 108.4 10–6 mm2/s (Fig. 3); the difference between the two groups was not statistically significant (P ¼ 0.10). No correlation between ADC, ADCHigh, and tumor size was observed as can be seen in Fig. 4a and b.
Discussion In this study, we found that ADC and ADCHigh did not correlate with patient age, tumor size, or tumor
histological characteristics. We also found that they did not yield significant discrimination between moderately and poorly differentiated lesions of head and neck SCCA. In a study of 81 patients Wang et al. (5) observed that malignant lymphomas had significantly lower ADC values compared to carcinomas of the head and neck. In the same study the mean ADC of carcinomas was significantly lower than that of benign solid tumors. The authors postulated that the differences in ADC values were related to factors which reduce the diffusivity of water molecules at a cellular level such as irregularly enlarged nuclei and increased cellularity, as observed in malignant tumors. They also state that the ADC of poorly differentiated carcinomas approached that of lymphomas owing to the increased cellularity observed in these high grade tumors when compared to lower grade counterparts (5).
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Fig. 3. A 57-year-old woman with a poorly differentiated (G3) SCCA of the tongue. The axial T2W MR image (a) and the gadoliniumenhanced T1W image (b) show a hyperintense mass lesion (arrows) originating from the right side of the tongue, which crosses the mid-line. The tumor is hyperintense on the b 900 diffusion-weighted image (c), with a mean ADC value of 983.85 10–6 mm2/s (d), below the mean ADC of our cohort.
Support for this preliminary observation can be seen in a study by Sumi et al. (7) in a small cohort of 25 patients with 25 histologically proven metastatic cervical lymph nodes, 25 benign lymphadenopathies, and five nodal lymphomas. They observed that lymph node ADC from highly or moderately differentiated carcinomas was greater than that from poorly differentiated carcinomas, which approximated that of malignant lymphomas. Similarly in a series of 28 patients with untreated SCCA of the head and neck, Kato et al. (15) found that the ADC values were significantly higher in well differentiated lesions compared to poorly differentiated ones. The tendency to lower ADC values in poorly differentiated tumors seen in our data, though not reaching statistical significance, is consistent with these earlier results. This is an interesting result in light of the fact that poorly differentiated tumors often exhibit a greater degree of nuclear irregularities, reduced extracellular matrix, and increased cellularity when compared to well differentiated tumors (13). The loss of keratinization, with loss of acellular eosinophil and intercellular
bridges in poor histologic grades makes tissues more compact, thus leading to lower ADC values (16). In fact our results are very similar to those of Yun et al. (16) who found a significant difference in the ADC values of moderately differentiated and poorly differentiated head and neck SCC, however, only when using b-values of 2000 s/mm2, which are higher than those routinely used in routine clinical practice, but probably required in DWI of head and neck tumors to disclose differences between different grade lesions. We can note that the ADC values in poorly differentiated tumors in that study were generally lower than we observed. One possibility therefore, is that there is a bias between our centers in the pathological rating of the tumors. We do not expect that the small difference between our maximum b-value (900 s/mm2) and that used in one part of their study (1000 s/mm2) would account for the observed difference. The use of additional b-values in our calculation of ADC could play a role as this would emphasize the contribution of intravascular motion and yield elevated ADC estimates.
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Fig. 4. Graph showing no correlation between: (a) tumor ADC and tumor size (P ¼ 0.23) and (b) between tumor ADCHigh and tumor size (P ¼ 0.29).
Studies in other body regions, in particular in the brain, prostate, and bone, have confirmed an inverse correlation between cellularity and ADC (17–19). We therefore suggest that the potential correlation between ADC values and tumor differentiation reflected by their grading in head and neck carcinomas may depend importantly on the choice of b-values (15,19–21). We further evaluated whether there were any differences in the correlations between ADC values and tumor histological characteristics by calculating mean ADCHigh values utilizing only b-values of 250 s/mm2 and above. By eliminating b-values of 0 and 50 s/mm2 we aimed to eliminate the perfusion contribution in the calculation of the ADCHigh values. Although ADCHigh values were lower compared to ADC values obtained utilizing all the b-values, no correlations were identified between ADCHigh values and clinical-histological
characteristics of squamous cell carcinoma of the oral cavity and oropharynx. A particular limitation of our study is that the patient cohort is small and not homogenous. Our cohort includes tumors from two different sites (oral cavity and oropharynx) and even though these tumors are both of squamous cell origin they may have different histological characteristics and natural history. Most of our tumors presented at an advanced T stage (76% T4) and TNM stage (84% Stage IV). This made other sub-groups very small and thus reduced the statistical strength of our analysis. Only two patients had well-differentiated tumors; the other tumors were distributed among the moderately and poorly differentiated groups. This limits our ability to compare with most other studies where comparison has been made between poorly and well differentiated tumors.
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For our statistical analysis we grouped the well differentiated tumors with the moderately differentiated group; a larger well differentiated tumor group could have helped to show any potential differences in ADC between tumors of different grading. When drawing tumor ROIs we did not formally evaluate the influence of necrosis on the ADC values. ROIs were drawn along the borders of the tumors in order to include the entire tumor volume irrespective of the presence of necrosis. This may have led to the high ADC values measured in the present study. In conclusion, in our cohort we did not observe significant correlations between ADC or ADCHigh values and clinical-histologic characteristics of SCCA of the oral cavity and oropharynx. Poorly differentiated tumors exhibited slightly lower mean ADC values compared to moderately differentiated tumors, however this difference did not reach statistical significance. Conflict of interest None declared.
Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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9. Vandecaveye V, De Keyzer F, Nuyts S, et al. Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: correlation between radiologic and histopathologic findings. Int J RadiatOncolBiolPhys 2007;67:960–971. 10. Hatakenaka M, Nakamura K, Yabuuchi H, et al. Pretreatment apparent diffusion coefficient of the primary lesion correlates with local failure in head-and-neck cancer treated with chemoradiotherapy or radiotherapy. Int J RadiatOncolBiolPhys 2011;81:339–345. 11. Gardesa A, Gale N, Nadal E. Squamous cell carcinoma. In: Barnes L, Eveson JW, Reichart P, et al (eds) Pathology & Genetics Head and Neck Tumours. World Health Organization Classification of Tumours. Lyon: IARC Press, 2005, pp.118–121. 12. Sobin L, Gospodarowicz M, Wittekind C. TNM Classification of Malignant Tumours. 7th edn. Wiley-Blackwell, 2009:24–38. 13. Akter M, Hossain S, Rahman QB, et al. A study on histological grading of oral squamous cell carcinoma and its co-relationship with regional metastasis. J Oral MaxillofacPathol 2011;15:168–176. 14. fsl 5.32, Analysis Group, Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK. 15. Kato H, Kanematsu M, Tanaka O, et al. Head and neck squamous cell carcinoma: usefulness of diffusionweighted MR imaging in the prediction of a neoadjuvant therapeutic effect. Eur Radiol 2009;19:103–109. 16. Yun TJ, Kim JH, Kim KH, et al. Head and neck squamous cell carcinoma: Differentiation of histologic grade with standard- and high-b-value diffusion-weighted MRI. Head Neck 2013;35:626–631. 17. Ginat DT, Mangla R, Yeaney G, et al. Diffusionweighted imaging for differentiating benign from malignant skull lesions and correlation with cell density. Am J Roentgenol 2012;198:W597–601. 18. Hayashida Y, Hirai T, Morishita S, et al. Diffusionweighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. Am J Neuroradiol 2006;27:1419–1425. 19. Wang XZ, Wang B, Gao ZQ, et al. Diffusion-weighted imaging of prostate cancer: correlation between apparent diffusion coefficient values and tumor proliferation. J Magn Reson Imaging 2009;29:1360–1366. 20. Nakanishi M, Chuma M, Hige S, et al. Relationship between diffusion-weighted magnetic resonance imaging and histological tumor grading of hepatocellular carcinoma. Ann SurgOncol 2012;19:1302–1309. 21. Guo AC, Cummings TJ, Dash RC, et al. Lymphomas and high-grade atrocytomas: comparison of water diffusibity and histologic characteristics. Radiology 2002;224: 177–183.
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Acta Radiol OnlineFirst, published on May 26, 2015 as doi:10.1177/0284185115587734
Original Article
Squamous cell carcinoma of the oral cavity and oropharynx: what does the apparent diffusion coefficient tell us about its histology?
Acta Radiologica 0(0) 1–8 ! The Foundation Acta Radiologica 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0284185115587734 acr.sagepub.com
Luke Bonello1, Lorenzo Preda2, Giorgio Conte1, Caterina Giannitto1, Sara Raimondi3, Mohssen Ansarin4, Fausto Maffini5, Paul Summers2 and Massimo Bellomi1,2
Abstract Background: Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about microstructural tumor characteristics. Purpose: To prospectively analyze the correlation between the apparent diffusion coefficient (ADC) and clinicalhistologic characteristics of squamous cell carcinoma (SCCA) of the oral cavity and oropharynx. Material and Methods: Sixty-seven patients with untreated, histologically proven SCCA of the oral cavity and oropharynx underwent conventional and diffusion-weighted (b-values 0, 50, 250, 500, and 900 s/mm2) MRI. Tumor ADC was calculated from regions of interest drawn manually on the highest b-value images using ImageJ (ImageJ, NIH) and fsl (fsl 4, University of Oxford) image processing packages. ADC was calculated in two ways: standard ADC using all b-values; and ADCHigh using only b-values 250 s/mm2. We assessed the correlations between both ADC and ADCHigh and the clinical-histological characteristics of SCCA. Results: Fifty-two patients (36 men, 16 women; mean age, 55 13 years) were suitable for ADC calculation. Mean ADC was 1136.0 108.5 10–6 mm2/s. Mean tumor ADCHigh was 991.2 152.1 10–6 mm2/s. Mean tumor size was 32.3 13.4 mm (range, 14.0–69.0 mm). We observed no correlation of either ADC or ADCHigh values with any of the clinical-histological tumor characteristics. Undifferentiated tumors (G3) showed lower apparent diffusion coefficient values compared to differentiated ones (G1-G2), without reaching statistical significance. Conclusion: We did not observe any statistically significant correlation between ADC values and clinical-histological characteristics of SCCA of the oral cavity and oropharynx.
Keywords Diffusion-weighted imaging, apparent diffusion coefficient, squamous cell carcinoma, oral cavity, oropharynx, grading Date received: 26 January 2015; accepted: 28 April 2015
Introduction Cancers of the oral cavity and oropharynx are the most common head and neck tumors in the United States (1). More than 90% of them arise from the mucosal lining and are classified as squamous cell carcinomas (SCCA) (2). As most patients with SCCA of the oral cavity and oropharynx already have a clinical and histologic diagnosis when they present for radiologic examinations, the role of cross-sectional imaging is largely to provide information about the local invasion of the tumor into the surrounding structures as well as the regional
1
Specialisation School of Radiology, University of Milan, Milan, Italy Department of Radiology, European Institute of Oncology, Milan, Italy 3 Department of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy 4 Department of Head and Neck Surgery, European Institute of Oncology, Milan, Italy 5 Department of Pathology, European Institute of Oncology, Milan, Italy 2
Corresponding author: L Bonello, Division of Radiology, European Institute of Oncology, Via Ripamonti 435, Milan 20141, Italy. Email: [email protected]
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spread of the disease, as both have an impact on treatment and prognosis (1). The presence of nodal metastases is the most significant predictor of adverse outcome in head and neck SCCA (3). In the pre-treatment evaluation, the biological activity of SCCA is categorized by tumor grading, a histopathologic parameter correlated with tumor aggressiveness and the risk of nodal and distant metastases (4). Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about tumoral microstructural characteristics without the use of ionizing radiation or exogenous contrast-agents. DW-MRI explores the random (Brownian) motion of water molecules in biological tissues, and allows the calculation of the apparent diffusion coefficient (ADC), with ADC values varying according to the microstructure and physiological state of tissues (5). DW-MRI has shown encouraging results for diagnosis, risk stratification, and therapy monitoring in several body tumors (6), with reports that measurement of ADC may be used for the characterization of primary head and neck lesions (5) as well as for the discrimination of metastatic from normal lymph nodes (7). DW-MRI has also shown good potential in detecting early response to therapy (8) as well as differentiating persistent or recurrent head and neck SCCA from non-tumoral tissue changes after (chemo-) radiotherapy (9). A recent study by Hatakenaka et al. (10) gave preliminary evidence that pre-treatment ADC, along with T stage, is a potential predictor of local failure in head and neck SCCA treated with radiotherapy, alone or combined with chemotherapy. The aim of our study was to prospectively analyze the correlation between the apparent diffusion coefficient obtained with DW-MRI with the clinical and histologic characteristics of SCCA of the oral cavity and oropharynx.
Material and Methods Patients Our Institutional Ethics committee approved this study, and written informed consent was obtained from each patient prior to enrolment. Between June 2007 and November 2011, we prospectively enrolled 67 consecutive patients with newly diagnosed, histologically proven, SCCA of the oral cavity and oropharynx, who presented to our department for a staging MR examination. From the MR examinations of these patients, ADC calculation was not feasible in nine patients due to the presence of artifacts caused by metallic dental-work. In an additional six patients ADC calculation was not
possible as the tumor was not visible on the high b-value images despite being visible on conventional T1-weighted (T1W) or T2-weighted (T2W) images, thus precluding the drawing of regions of interest (ROIs). The remaining 52 patients (36 men, 16 women; mean age, 55 13 years) were included in the study.
Histology The final diagnosis for all patients was based on pathological findings obtained from the surgical specimens. Each specimen was fixed in buffered formalin for 24 h, paraffin embedded, cut to 5 micron thick sections and stained with hematoxylin and eosin. For every specimen, the histotype of the neoplasm, the anatomical structures involved, and grading were evaluated. The histotype of each tumor was defined according to the WHO classification of tumors (11). Tumor staging was performed according to the revised TNM staging system VII edition (12). Tumor grading was performed according to Broder’s classification; G1 when the undifferentiated component was less than 25% of the neoplasm, G2 when the undifferentiated component was less than 50% of the neoplasm, and G3 when the undifferentiated component was less than 75% (13).
MR technique All exams were performed on a 1.5 T MR scanner (Avanto, Siemens Medical Systems, Erlangen, Germany). Patients were examined in the supine position using a phased array coil. Based on a survey scan, the following MR images were acquired: axial T2W turbo spin echo (TSE) with radial trajectory filling of k-space (TR/TE, 6500/109 ms; slice thickness/gap, 3/0.6 mm; matrix, 320 320; acquisition time, 8 min 14 s) volume acquisition ranging from frontal sinus to pulmonary apices; axial T1W TSE (TR/TE, 500/14 ms; slice thickness/gap, 3.0/0.6 mm; matrix, 320 240; acquisition time, 6 min 8 s) with coverage to the T2W sequences; coronal and/or sagittal T2W TSE with radial trajectory filling of k-space (TR/TE, 3900/ 150 ms; slice thickness/gap, 3/0.6 mm; matrix, 320 320; acquisition time, 4 min 53 s) with range aimed on the volume of interest; diffusion weighted images (TR/TE, 5000/77 ms; slice thickness/gap, 5/1 mm; matrix, 160 130; acquisition time, 4 min 35 s) with 5 b-values (0, 50, 250, 500, 900 s/mm2) and fat suppression using a SPAIR technique (Spectral selection Attenuated Inversion Recovery); and an ultra-fast 3D T1W spoiled gradient echo scan with isotropic voxel dimensions following administration of contrast agent (TR/TE, 7.43/2.88 ms; flip angle, 12 ; matrix, 384 276; slice thickness/gap, 0.6/0.03 mm;
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acquisition time, 3 min 36 s). The T1 post-contrast sequence was utilized to generate 2 mm thick multiplanar reconstructions in the axial, coronal, and sagittal planes. All the sequences except the T1W post-contrast sequence utilized parallel imaging with the GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) algorithm with an acceleration factor of 2. The contrast agent used was gadopentate dimeglumine (Magnevist; Bayer Schering Pharma AG, Berlin, Germany) 0.2 mmol/kg and was administered at a flow rate of 2 mL/s, followed by a 20 mL bolus of saline solution at a flow rate of 2 mL/s.
Image and data analysis All examinations were anonymized and transferred to a workstation for analysis. A radiologist with 2 years of experience in head and neck MRI utilized all sequences available to identify the tumor. Tumor size was measured as the largest transverse diameter on the axial T1 post-contrast images.
The diffusion-weighted sequences and corresponding ADC maps were further transferred to a personal computer for evaluation of ADC values. Besides the standard calculation of the ADC map using all b-values (0, 50, 250, 500, 900 s/mm2) a map was calculated using only b-values of 250, 500, and 900 s/mm2 (ADCHigh). Regions of interest were carefully defined (Image J, NIH) to isolate the tumor from the surrounding tissues on all sections in which it was present based on the highest diffusion weighting (b-value ¼ 900 s/ mm2) images. The volumes obtained in this way were saved and used to extract mean, median, and standard deviation of the ADC values from the two maps for each tumor by scripts developed in-house based on the FMRIB Software Library (14) (Fig. 1).
Statistical analysis Baseline characteristics of patients and tumors were expressed as mean and standard deviation for continuous variables and as frequency and percentage for
Fig. 1. Illustration showing calculation and extraction of tumoral ADC values. The post-contrast axial T1W, fat-suppressed GE image (a) and axial T2W image (b) show a large tumor arising from the right side of the tongue extending past the midline. The diffusionweighted sequence with various b-values (b ¼ 0, 50, 250, 500, and 900 s/mm2; c–g, respectively) show a hyperintense signal within the tumor compared to the surrounding tissues. All images were exported in DICOM format to ImageJ, where ROI’s were manually drawn by the radiologist around the tumor margins on the high b-value (900 s/mm2) for every slice (h) in which the tumor was visible using the T1W and T2W images for reference. For each DWI scan, voxel-wise calculations of ADC (dashed line) and ADCHigh (dotted line) were respectively made using all five b-values (solid circles) and just the b-values of 250 and higher (Xs) as the latter may more accurately reflect the behavior of a slow diffusing ‘‘tissue water’’ compartment in a biexponential model (solid line) of in vivo water motion (i). The ADC and ADCHigh values calculated were then utilized to create maps for ADC and ADCHigh (j, k). The ADC and ADCHigh values within the set of ROIs, as illustrated by the respective histograms (l, m) were extracted and used to calculate mean, median, and standard deviation values for the tumor.
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categorical variables. The normality of the ADC and ADCHigh values distribution was verified by the Kolmogorov-Smirnov test. The association between ADC and ADCHigh values and histological characteristics of the tumors was assessed by T-tests for analyses with two groups, and one-way ANOVA for analyses with more than two groups. Since there were only two well differentiated tumors in our series, G1 and G2 lesions were grouped together for the statistical analysis. The correlation of ADC and ADCHigh values with patients’ age and tumor size was assessed using the Pearson correlation coefficient. P values < 0.05 were considered statistically significant. The analysis was performed with SAS (Statistical Analysis System) Software (SAS Institute Inc., Cary, NC, USA) version 9.2.
Results Tumor characteristics Out of the 52 tumors analyzed, 43 were located in the oral cavity and nine in the oropharynx. Mean tumor size was 32.3 13.4 mm (range, 14.0–69.0 mm). The histological characteristics regarding T, N status, and TNM stage are summarized in Table 1.
Table 1. Baseline characteristics of the study population. Tumors characteristics (n ¼ 52)
Mean (SD)
Size (mm)
32.3 (13.4) Tumors, n (%)
Grading 1 2 3 N 0 1 2 3 T 1 2 3 4 TNM stage I II III IV
2 (4%) 22 (42%) 28 (54%) 18 9 23 1
(35%) (18%) (45%) (2%)
1 9 1 41
(2%) (17%) (2%) (79%)
1 5 3 42
(2%) (10%) (6%) (82%)
Correlation between ADC and clinical-histologic tumor characteristics The mean tumor ADC value was: 1136.0 108.5 106 mm2/s. The mean tumor ADCHigh value was 991.2 152.1 10–6 mm2/s. There was no significant
Table 2. Association between ADC and histological characteristics. Histological characteristics Grading 1/2 3 N 0 1 2/3 N (0/1 vs. 2/3) 0/1 T 1/2 3/4 TNM stage I/II/III IV
ADC mean (SD)
P value 0.10
1162.5 (104.6) 1113.3 (108.4) 0.23 1133.1 (134.8) 1186.1 (100.7) 1113.7 (82.4) 0.21 1150.8 (125.1) 0.16 1179.4 (117.5) 1125.7 (105.1) 0.90 1139.3 (161.8) 1132.0 (95.1)
Student t-test for analysis with two groups and one-way ANOVA for the analysis with more than two groups.
Table 3. Association between ADCHigh and histological characteristics. Histological characteristics Grading 1/2 3 N 0 1 2/3 N (0/1 vs. 2/3) 0/1 T 1/2 3/4 TNM stage I/II/III IV
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ADCHighmean (SD)
P value 0.82
985.9 (162.4) 995.7 (145.6) 0.92 1006.2 (113.4) 981.2 (218.1) 998.5 (139.3) 0.99 997.9 (152.3) 0.28 944.0 (161.8) 1002.4 (149.5) 0.31 953.3 (128.2) 1007.8 (147.8)
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Fig. 2. A 62-year-old woman with a moderately differentiated (G2) SCCA of the retromolartrigone. The axial T2W MR image (a) shows a hyperintense mass lesion (arrows) in the retromolartrigone. On the gadolinium-enhanced T1W image (b), the mass is seen to infiltrate the right sub-lingual space, the medial pterygoid, and masseter muscles, as well as the mandibular ramus. The tumor is hyperintense on the b 900 diffusion-weighted image (c) with a mean ADC value of 1245.35 10–6 mm2/s (d), above the mean ADC of our cohort.
correlation of ADC and ADCHigh with patient age (P ¼ 0.43 and 0.76, respectively). No significant associations between ADC, ADCHigh, and histological characteristics of the tumors were observed (Tables 2 and 3). Since there were only two well differentiated tumors in our series, G1 and G2 lesions were grouped together for the statistical analysis. The mean ADC value of G1 þ G2 tumors was 1162.5 104.6 10–6 mm2/s (Fig. 2) whereas the mean ADC of G3 tumors was 1113.3 108.4 10–6 mm2/s (Fig. 3); the difference between the two groups was not statistically significant (P ¼ 0.10). No correlation between ADC, ADCHigh, and tumor size was observed as can be seen in Fig. 4a and b.
Discussion In this study, we found that ADC and ADCHigh did not correlate with patient age, tumor size, or tumor
histological characteristics. We also found that they did not yield significant discrimination between moderately and poorly differentiated lesions of head and neck SCCA. In a study of 81 patients Wang et al. (5) observed that malignant lymphomas had significantly lower ADC values compared to carcinomas of the head and neck. In the same study the mean ADC of carcinomas was significantly lower than that of benign solid tumors. The authors postulated that the differences in ADC values were related to factors which reduce the diffusivity of water molecules at a cellular level such as irregularly enlarged nuclei and increased cellularity, as observed in malignant tumors. They also state that the ADC of poorly differentiated carcinomas approached that of lymphomas owing to the increased cellularity observed in these high grade tumors when compared to lower grade counterparts (5).
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Fig. 3. A 57-year-old woman with a poorly differentiated (G3) SCCA of the tongue. The axial T2W MR image (a) and the gadoliniumenhanced T1W image (b) show a hyperintense mass lesion (arrows) originating from the right side of the tongue, which crosses the mid-line. The tumor is hyperintense on the b 900 diffusion-weighted image (c), with a mean ADC value of 983.85 10–6 mm2/s (d), below the mean ADC of our cohort.
Support for this preliminary observation can be seen in a study by Sumi et al. (7) in a small cohort of 25 patients with 25 histologically proven metastatic cervical lymph nodes, 25 benign lymphadenopathies, and five nodal lymphomas. They observed that lymph node ADC from highly or moderately differentiated carcinomas was greater than that from poorly differentiated carcinomas, which approximated that of malignant lymphomas. Similarly in a series of 28 patients with untreated SCCA of the head and neck, Kato et al. (15) found that the ADC values were significantly higher in well differentiated lesions compared to poorly differentiated ones. The tendency to lower ADC values in poorly differentiated tumors seen in our data, though not reaching statistical significance, is consistent with these earlier results. This is an interesting result in light of the fact that poorly differentiated tumors often exhibit a greater degree of nuclear irregularities, reduced extracellular matrix, and increased cellularity when compared to well differentiated tumors (13). The loss of keratinization, with loss of acellular eosinophil and intercellular
bridges in poor histologic grades makes tissues more compact, thus leading to lower ADC values (16). In fact our results are very similar to those of Yun et al. (16) who found a significant difference in the ADC values of moderately differentiated and poorly differentiated head and neck SCC, however, only when using b-values of 2000 s/mm2, which are higher than those routinely used in routine clinical practice, but probably required in DWI of head and neck tumors to disclose differences between different grade lesions. We can note that the ADC values in poorly differentiated tumors in that study were generally lower than we observed. One possibility therefore, is that there is a bias between our centers in the pathological rating of the tumors. We do not expect that the small difference between our maximum b-value (900 s/mm2) and that used in one part of their study (1000 s/mm2) would account for the observed difference. The use of additional b-values in our calculation of ADC could play a role as this would emphasize the contribution of intravascular motion and yield elevated ADC estimates.
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Fig. 4. Graph showing no correlation between: (a) tumor ADC and tumor size (P ¼ 0.23) and (b) between tumor ADCHigh and tumor size (P ¼ 0.29).
Studies in other body regions, in particular in the brain, prostate, and bone, have confirmed an inverse correlation between cellularity and ADC (17–19). We therefore suggest that the potential correlation between ADC values and tumor differentiation reflected by their grading in head and neck carcinomas may depend importantly on the choice of b-values (15,19–21). We further evaluated whether there were any differences in the correlations between ADC values and tumor histological characteristics by calculating mean ADCHigh values utilizing only b-values of 250 s/mm2 and above. By eliminating b-values of 0 and 50 s/mm2 we aimed to eliminate the perfusion contribution in the calculation of the ADCHigh values. Although ADCHigh values were lower compared to ADC values obtained utilizing all the b-values, no correlations were identified between ADCHigh values and clinical-histological
characteristics of squamous cell carcinoma of the oral cavity and oropharynx. A particular limitation of our study is that the patient cohort is small and not homogenous. Our cohort includes tumors from two different sites (oral cavity and oropharynx) and even though these tumors are both of squamous cell origin they may have different histological characteristics and natural history. Most of our tumors presented at an advanced T stage (76% T4) and TNM stage (84% Stage IV). This made other sub-groups very small and thus reduced the statistical strength of our analysis. Only two patients had well-differentiated tumors; the other tumors were distributed among the moderately and poorly differentiated groups. This limits our ability to compare with most other studies where comparison has been made between poorly and well differentiated tumors.
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For our statistical analysis we grouped the well differentiated tumors with the moderately differentiated group; a larger well differentiated tumor group could have helped to show any potential differences in ADC between tumors of different grading. When drawing tumor ROIs we did not formally evaluate the influence of necrosis on the ADC values. ROIs were drawn along the borders of the tumors in order to include the entire tumor volume irrespective of the presence of necrosis. This may have led to the high ADC values measured in the present study. In conclusion, in our cohort we did not observe significant correlations between ADC or ADCHigh values and clinical-histologic characteristics of SCCA of the oral cavity and oropharynx. Poorly differentiated tumors exhibited slightly lower mean ADC values compared to moderately differentiated tumors, however this difference did not reach statistical significance. Conflict of interest None declared.
Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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