Histopathology 1977, I, 431-443

Histological discrimination of malignancy in rnucinous ovarian tumours A . A G R O F O J 0 BL ANC O, A . C . C . G I B B S & F.A.LANGLEY* The Department of Pathology, St. Mary’s Hospital, Manchester and The Department of Community Medicine, University of Manchester Accepted for publication 23 August 1977 BLANCOA.AGROFOJO, GIBBSA.C.C. & LANGLEY F.A. (1977) Histopathology 43 1-443 Histological discrimination of malignancy in mucinous ovarian tumours

I,

Eight histological features were measured quantitatively in a group of 77 ovarian mucinous carcinomas and in a group of 28 benign mucinous cystomas. These were compared with the duration of survival of the patients and the clinical staging of the tumours. Using the method of discriminant function analysis it was possible to identify a group of tumours of ‘borderline malignancy’. Although single histological features were inadequate indicators of prognosis a combination of three features proved fairly accurate. Keywords : ovarian turnours, malignancy, rnucinous tumours, discriminant function analysis

Introduction In the World Health Organization’s classification of ovarian tumours each type of common epithelial tumour is divided into three groups : benign, borderline and malignant (Serov & Scully 1973). The terms ‘borderline malignancy’ or ‘of low malignant potential’ are applied to a neoplasm which has some, but not all, the features of malignancy, such as stratification or multilayering of epithelium, apparent detachment of cellular clusters from their sites of origin (budding), mitotic activity and nuclear abnormalities, but lacking clear evidence of stromal invasion. Tumours with such features tend to run a more indolent course than those which are more obviously malignant (Munnell & Taylor 1949, Santesson & Kottmeier 1967). However, the histological evaluation of such tumours is highly subjective, rendering comparison from one centre to another of little value. Indeed, Malloy, Dockerty, Welch & Hunt (1965) stated that their experience of papillary tumours has enabled them to establish microscopic criteria of benignancy, thereby eliminating the so-calIed ‘borderline group’. There is therefore a problem in laying down objectivehistological criteria to identify this group of neoplasms. * Address for reprints : Professor F.A.Langley, Department of Pathology, St. Mary’s Hospital, Whitworth Park, Manchester M13 OJH.

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A.A.Blanco, A.C.C.Cibbs and F.A.Langley

A parallel problem is that of prognosticating the outcome for a patient, once a malignant ovarian tumour has been diagnosed. Histologically various grading criteria have been used (Hertig & Gore 1961, Dyson, Beilby & Steele 1971)but these have not proved very satisfactory. More objective criteria have been used such as counting the number of nucleoli per cell (Taylor & Long 1955) or counting the , frequency of mitoses (Dyson et al. 1971); whilst Anderson & Langley ( I ~ o )using semiquantitative methods found that in mesonephroid tumours, the more cellular and the more papillary the tumour the poorer the outcome. Although such studies indicate, in statistical terms, which tumours are to be regarded as more malignant and which less, they are not very accurate in prognosticating the course of an individual tumour. The object of the present investigation is to attempt to give objective meaning to the term ‘turnour of borderline malignancy’ and to provide criteria for assessing the course of individual neoplasms. For this purpose we have confined our studies to one histological type of ovarian tumour, namely the mucinous tumours and, whilst not ignoring the simpler analytical techniques, we have employed discriminant functional analysis to eight quantitatively assessable variables.

Material and methods The clinical observations and histological slides of 77 women who had been operated on at St. Mary’s Hospital, Manchester, in the last 20 years for mucinous ovarian carcinomas were reviewed, together with a control group of 28 patients with histologically benign mucinous cystomas selected at random over the same period. For this study the two most important clinical observations used were: ( I ) Duration of survival: determined from the patient’s notes either at St. Mary’s Hospital or at the radiotherapy centre (the Christie Hospital, Manchester) or from the patient’s general practitioner or from the Central Register Office. (2) Clinical staging using the FIG0 classification : assessed retrospectively from the patient’s notes. A variable number of histological slides was available from each tumour, depending on its size and the inclination of the examining pathologist. Eight features were measured : I the percentage of epithelial tissue in the tumour (the ‘cellularity’)

the percentage of connective tissue 3 the papillarity 4 the degree of epithelial budding 5 the frequency of abnormal nuclei 6 the extent of multilayering 7 the frequency of mitoses 8 the average number of nucleoli per nucleus. 2

These were measured as follows: Sections were examined at total magnification of 40 using a wide angle ocular ( x 10) so that the area of each field was 4.5 mm’. A graticule in the ocular was

Discrimination of malignancy in ovarian tumouvs

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marked with 2 j randomly arranged dots within a ring. The number of dots falling on epithelium, connective tissue and the intervening (luminal) space was recorded for each of 2 j randomly selected fields, and the percentage ofdots falling on epithelium or connective tissue was calculated for the 2 j selected fields. Fields of necrotic tissue were not included. Papillary structures were estimated semiquantitatively. Twenty-five fields were examined at total magnification of x LOO (area I .7 mm’) and, if papillary structures were seen, the field was recorded as positive and if none were seen as negative. Hence four times the number of positive fields was taken as the percentage of papillary structures. Budding is a term used for the formation of little clusters of epithelial cells lying free or attached by a very narrow pedicle to the epithelial surface (Figure I). When a field ( x roo) contained no clusters it was scored 0 , when only a few clusters were present it was scored I and when many were found a score of 2 was recorded. Twenty-five fields were examined-hence the maximum score was 50, thus twice the total score measures the percentage budding. Abnormal nuclei and multilayering were assessed in a similar manner. Mitoses were counted in 25 fields at a magnification of 400 (field size 0.28 mm’) and the number of abnormal mitoses was recorded. The above measurements were made on sections stained with H & E, but to estimate the mean number of nucleoli per nucleus, sections were stained by the Unna-Pappenheim method and examined under oil immersion at a magnification of

Figure I. Budding of the epithelium of a borderline inucinous tumour. H & E C

x 150.

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A .A.Blanco, A . C.C.Gibbs and F.A.Langley

Two hundred nuclei were examined and an average nucleolar count was calculated. 1000.

DISCRIMINANT FUNCTION ANALYSIS

This is a technique for distinguishing between subgroups in a population (Seal 1964, Armitage 1971),e.g. to find the best combination of quantitative measurements that will distinguish benign from malignant tumours or, among malignant tumours, between those which will kill patients earlier (more malignant) from those which will kill patients later (less malignant). Thus, for malignant mucinous tumours, given the value of the eight variables described above, denoted by xl,x2, . . . . . xs, it is possible to calculate a linear function z based on these variables with coefficients b , , b 2 , . . . . . b8. where z = blxl + b2x, + . . . . . + b,x, . . . . . (Equation I). and a value D such that, if z is less than D, the tumour is allocated to the less malignant group, and, if z is greater than D,it is allocated to the more malignant group. This is done in such a way as to separate the groups to their maximum extent, and to reduce the misallocation of tumours to a minimum. When there are two groups, as in this case, this can be done with a single linear combination as in (I). The results can be examined to see whether or not the discrimination between the two groups is greater than would be expected by chance. In arriving at the appropriate linear combination certain assumptions are made : I that the variables (measurements) are normally distributed, and 2 that the co-variance matrices of the two subgroups of the population are the same. For certain measurements in the population it is clear that asssumption I is not correct and to try to remedy this some measurements have been replaced by transformed values, e.g., by their square roots. Further, in certain of the analyses (particularly those between the benign and malignant group) assumption 2 is not justified but this is difficult to remedy. Since this procedure is for discriminating between groups it is not expected that it will correctly allocate each individual to its group, but it should be better than a purely chance allocation. In practice there is little overlap in the different analyses and the extent of this overlap, measured by the cases misclassified, is an index of the efficiency of discrimination.

Results STAGES

Figure 2 shows the survival curves for patients with malignant tumours according to their clinical stage. These were calculated by the method of Berkson & Gage (1950) and the confidence limits by those of Greenwood (1926) and Ederer (1961). It can readily be seen that the clinical stage fairly accurately prognosticates the course of the disease for the majority of patients but a few in stage I die early and a few in stage I1 survive for a relatively long time. The curve for stage 1exaggerates the mor-

Discrimination of malignancy in ovarian tumours

435

100-

-----___ ----- _ _ _ _ _ _ _ _-80

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Figure 2. Survival curves for patients with rnucinous ovarian carcinomas.

0

2

4

6 Years

8

10

12

tality from the tumour since it is not corrected for the expected mortality of a normal population. This point is emphasized when the benign tumours are considered. Of 28 such patients six had died at the time of follow-up. Three of them had lived over 10years after the removal of the tumour and two were aged 71 years at the time of operation and one 51 years. The other three died at I year 6 months, 2 years 7 months and 4 years 7 months post-operatively and were age 62, 61 and 37 years respectively at the time of operation. The causes of death were not known. There were only two patients with stage IV disease, so that it was impossible to construct a survival curve for this group. Table I. The relation between three histological features and the clinical stage

Cellularity

Budding

Mitoses

Nucleoli

Benign

i5.5k9.1

4.56-+ 11.8

*

0.79 k0.19

Stage I II I11

35.3 k 13.2 60.24k14.4 63.9 t 9 . 2

3.8 k 5 . 1 14.0k19.1 20.8 f 2 2 . 0 2

18.8 k 13.1 31.8k16.8 53.3 -+ 16.5

~~

Units: Cellularity-percentage of field Budding-see text Mitoses in 25 fields Nucleoli per nucleus * Only one turnour showed any mitoses

1 . 1 1 k0.29 1.18_+0.31 1.34f0.27

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AABlanco, A.C.C.Gibbs and F.A.Langley

CELLULARITY

Table I shows that, although the epithelial cellularity of these neoplasms increases progressively with the stage of the disease, there is a very wide range of values so that, except with very low values, it is not possible to foretell the outcome. This is illustrated visually in Figure 3. BUDDING

This presumably indicates excessive epithelial proliferation relative to connective tissue. Table T shows that although this is much increased in stages I1 and 111 coinpared with the benign tumours and those in stage 1, there is such a wide range of values that there is poor discrimination. MITOSES

These are extremely rare in benign tumours, being seen in only one of our cases and

benign

I4E 10

fl

r

stage I

t

n

stage 111

2 010

30

50

70

Cellularity

Figure 3. Cellularity and mitotic 30 50 70 90 activity of mucinous ovarian Mitoses in 25 fields carcinomas by stage.

90 0 10

Discrimination of malignancy in ovarian turnours

437

Table 2. Mitotic activity by stage and survival Stage IV

Stage I

Stage I1

Stage 111

All tumours

18.8 f 13.1 (37)

31.8 k 16.8 (17)

53.3 k 16.5 (21)

Patients dying in < 5 Yrs

21.4

(5)

35.3

(11)

53.8

Surviving 5 yrs

19.8

(24)

22.2

(4)

Alive but follow up < 5 Yrs

14.4

(8)

33.5

(2)

41.5 (2)

(20)

45

41.5 ( 2 )

(1)

The numbers in brackets are the number of patients in each group.

then only at a rate of three mitoses in 25 fields. Table I analyses the mitotic count stage by stage and shows a progressive increase with stage but considerable overlap in range. This is illustrated in Figure 3 from which it can be seen that only at extreme ranges can a prognosis be hazarded. Table 2 shows that, for a given stage, the mean mitotic index is lower the longer the survival. NUCLEOLAR COUNT

This shows the same general trend as the other variables and is not dissimilar to the observations of Taylor & Long (1955)on nucleolar counts in serous tumours (Table 1).

Because these variables taken independently do not indicate the outcome for an individual patient, they were then combined and analysed by the method of discriminant analysis. Frequency *Or

Figure 4. Comparison of benign and malignant mucinous tumours using all eight variables six of which are transformed. The score ( z ) is measured along the abscissa and the number of tumours for each value of z along the ordinate. I - benign; -4 - - - - malignant.

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A .A .Blanco, A . C.C.Gibbs and 1;:A .Langley

DISCRIMINATION BETWEEN BENIGN A N D MALIGNANT TUMOURS

Several analyses were made using different combinations of variables. In some the original crude measurements were used but, in others, the square root of the measurement was employed. The analysis which discriminated best between benign and malignant tumours included all eight variables, six of which were transformed using the square root, but the mitotic and nucleolar counts were untransformed. The score ( z in equation I) was calculated for each case and a histogram (Figure 4) constructed showing the number of cases for each score. Each group forms a separate cocked-hat histogram with some overlap between values of z from +0.5 to +2.0. The arrow indicates the value of z which best discriminates between benign and malignant tumours. One histologically benign tumour (3.6% of the group) lies on the malignant side of the arrow and this patient (aged 62 years) died I year 6 months postoperatively. Four histologically malignant tumours ( 5 . 2 % of the group) lie on the benign side of the arrow and of these, two were still alive after 4 years 5 months’ and 4 years 3 months’ follow-up. The expected extent of the miscalculation is 1.9%, which is in fair agreement with the observations. The overlapping areas of the histogram may be considered as defining the borderline group of tumours and the eight for which z lies between + 0.5 and 2.0 may be regarded as falling into this category. The extent of the overlap and the level of discrimination depend on how many variables are used and how the variables are manipulated mathematically. Thus, in Figure 5, the same eight variables have been used only two were transformed; there is an overlap of only five cases, but 15 histologically malignant tumours lie on the benign side of the arrow.

+

DISCRIMINANT F U N C T I O N ANALYSIS OF T H E M A L I G N A N T G R O U P

Excluding nine patients who were not followed up for a sufficient length of time, the median survival time of the remaining 68 patients was between 3 years 4 months and 3 years 5 months. The 34 patients who lived less than the median survival time formed Frequency

r-I

I

I

I

8

_ _ _ _ _ _ _ _J

Figure 5. Comparison

Discrimination of malignaacy in ovarian turnours

439

the ‘more’ malignant group and those who lived longer than this formed the ‘less’ malignant group. Figure 6 shows the division between the two groups using all eight variables. Two distinct cocked-hat histograms are formed, the modes are well separated but there is some overlap and therefore some misclassification, four of the more malignant tumours lie among the less malignant group and three of the less malignant tumours are classified with the more malignant neoplasms. Whilst the median survival time is a convenient dividing line between the more and the less malignant tumours, its value varies from one study to another and it is probably better, therefore, to use the more conventional division of 5 years. This reduces the number of individuals from 68 to 65 because three have not been followed up for the required length of time. The discriminant function analysis programme from the SPSS (Statistical Package for the Social Sciences) was used. This programme has the facility for using a stepwise procedure for the selection of the variables for the desired discrimination. It starts by selecting the variable which discriminates best between the two groups and then selects a second variable that, in combination with the first, increases the discrimination between the two groups. The process continues, selecting one variable at a time until all possible variables have been included. Since variables are only added one at a time, at any stage of the process the variables included may not necessarily be the best possible combination out of all those possible with that number of variables but it is likely that the combination of variables will be close to, or the same, as the best combination. The transformation sin-1,/& where x is the initial variable was applied to all the variables except the number of mitoses and of nucleoli. This transformation is designed to produce a new variable which has a similar variance in both the less and the more malignant groups, since equality of variance in the underlying populations is one of the statistical assumptions made in the use of discriminant analysis. A slight problem occurred because the number of nucleoli per nucleus was not assessable in five cases because only H & E stained sections were available. A possible approach is to ignore the results for these cases when the discriminant function is calculated, but this of course has the disadvantage of reducing the number Frequency

----I

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I

’II

12

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Figure 6. Comparison between ............. the ‘more’ and the ‘less’ malignant groups of tumours using I I -3 all eight variables. - - - -‘more’-4 malignant (death within 3.45 years); . . . . .‘less’ malignant.

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A .A .Blanco, A . C.C.Gibbs and F. A .Langley

of cases on which it is based. An alternative is to substitute for the unknown value the mean value for the group of which it is a member. These two approaches gave very similar results and the second was adopted. For those five patients where the number of nucleoli per cell was missing the overall mean number of nucleoli per cell for all results was used when the patients were classified by the programme into less malignant and more malignant groups. The results of the different analyses are shown in Table 3 . It is interesting to note that the variable with missing information is brought in last when all eight variables are used. Column A shows that the estimated percentage misclassified decreases as the number of variables increases, but the decrease becomes small as the number of variables becomes large. Clearly, the addition of more and more variables has little effect in reducing the percentage misclassified. The actual percentage misclassified in the less malignant group shown in column B, decreases as the number of variables increases, and then rather surprisingly increases, but this increase represents only a single misclassified individual. The actual percentage misclassified in the more malignant group shown in column C remains constant as the number of variables increases, and then increases, but again this increase represents only a single misclassified individual. Because the basic eight variables are more variable in the more malignant compared with the less malignant group, the corresponding discriminating scores are also more variable in the more malignant group, despite the transformation applied Table 3. shows the variables used on different analyses and the percentages misclassified when 60 results are used No. of variables used I

2

3

4 5

6 7 8

Variables used NA NA, EP NA, EP, NA, EP, NA, EP, NA, EP, NA, EP, NA, EP,

MI MI, CT MI, CT, PA MI, CT, PA, MU MI, CT, PA, MU, BU M1, CT, PA, MU, BU, N U

A

B

C

18.1

14.3

18.9

13.75

10.7 10.7

18.9

12.5 12.0

11.7

11.5 11.3 11.2

18.9 18.9

7 .I 7. I 10.7

21.6

10.7 10.7

21.6 21.6

NA = Nuclear Abnormality EP = Epithelium (cellularity) MI = Mitoses CT = Connective Tissue PA = Papillarity MU = Multilayering BU =Budding N U = Nucleoli = Estimated expected percentage misclassified in each group A = Actual percentage misclassified in the less malignant group B C = Actual percentage misclassified in the more malignant group.

18.9

Discrimination o j ’ malignancy in ovarian tuiwours

44 I

to six of the variables to counteract this. This leads to the consistent difference in the percentage misclassified in the two groups. Since, in the discriminant analyses shown in Table 3, the variable NU is included only in the last step, it seems reasonable to exclude this variable from consideration, and to reanalyse the data using results from all 65 patients. The results from this analysis (not shown) were very similar to those shown in Table 3. A possible approach to determine when to stop including further variables, would be to stop when the addition of further variables results in no further decrease in the estimated expected percentage misclassified. Using this approach we should include all eight variables. However we should be including variables which contributed very little indeed to the discrimination between the groups. A more useful approach would be to include a further variable only when it makes a statistically significant improvement in the discrimination between the groups. Using the 0.05 probability level, for the analyses given in Table 3 we should not carry out any further analyses beyond that based on the three variables NA, EP, MI. The addition of the further variable CT does not significantly improve the discriniination between the two groups. Table 3 shows that the estimated expected percentage misclassified only decreases from 12.5% when 3 variables (NA, EP, MI) are used to 1 I .2% when eight variables (NA, EP, MI, CT, PA, MU, BU, NU,) are used, a change of I .3 percentage points. This means that we can do almost as well in discriminating between the groups using three variables as when using eight. In examining the effectiveness of this discrimination procedure, it should be borne in mind that our two groups of less malignant and more malignant are not sharply delineated from each other. We should expect that patients near the arbitrary borderline of 5-year survival in either group are more likely to be misclassified than patients far away from it. Clearly it is of interest to examine the patients who are misclassified by the programme in terms of their period of survival. Of the three patient who survived for more than 5 years and whom the programme predicts would survive for less than 5 years, one patient was still alive after 6 years follow-up, one had died after 6 years, and one after 8 years. Of the patients who did not survive for more than 5 years and whom the programme predicts would survive for more than 5 years, one survived for 4 years 8 months, one for 3 years 7 months, and one for 3 years 4 months. The remaining four patients survived only for periods of between 8 months and 2 years I month.

Discussion T H E B O R D E R L I N E G R O U P OF M U C I N O U S T U M O U R S

The term ‘borderline tumour’ is a histological description applied to neoplasms which whilst not invasive, show some features of malignancy and it implies that the histologist is uncertain of their malignant potential. In the above analysis it has been shown that a small group of tumours can be identified using quantitative measurements, more or less objectively assessed, which conforin to this concept. The size of

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A .A . Blanco, A . C.C.Gibbs and 1;.A Langley

the group varies with the number of variables measured and how they are manipulated mathematically, since in the ordinary run of laboratory work a histologist uses a number of subjectively judged variables when assessing the malignancy or benignancy of a neoplasm, it is probable that his group of borderline tumours will be larger than that determined quantitatively. Moreover, there will be variation between pathologists which will make the assessment of the size of such a group differ from laboratory to laboratory. However, it must be remembered that even quantitative measurements are subject to personal error and will vary from microscopist to microscopist. Nevertheless, in an epidemiological study or therapeutic trial it should be possible using quantitative methods to standardize the borderline group and hence the division between truly benign and truly malignant. T H E ASSESSMENT OF P R O G N O S I S

Our results show a fairly close relation between the clinical staging of the tumour (albeit determined retrospectively) and the outcome for the patient, Like others we found that the worse the outcome the more cellular the tumour, the more frequent the mitoses, the more numerous the nucleoli and so on, but there is so much scatter except at the widest extremes of these factors that no assessment of prognosis can be made using these individually. However, by combining these factors and constructing a discriminant function a fairly satisfactory prognosis can be made in the majority of cases. It is interesting to note that, although we used eight factors, a combination of only three factors afforded a satisfactory assessment of prognosis. This indicates that the combination of only a few factors which are relatively easy to measure gives useful discrimination. Whether such an assessment is better than a grading system such as that of Hertig & Gore (1961), cannot be answered without further investigation but, because grading systems vary from one group of workers to the next, it seems likely that a quantitative system such as this could be more readily standardized for large scale studies. However, before discriminant analysis can be established as a useful tool it needs to be assessed prospectively and also by other histologists to eliminate, or minimize, the effects of personal error. In this particular study all the measurements were carried out by one microscopist.

Acknowledgement We wish to thank the Manchester University Department of Medical Illustration for preparing the histograms.

References ANDERSON M.C. & LANGLEY F.A. (1970) Mesonephroid tumours of the ovary. Journal of Clinical Pathology 23, 2 10-218 ARMITAGE P. (1971) Statistical Methods in Medical Research, pp. 322-339. Blackwell, Oxford

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BERKSON J. & GAGER.P. (1950) Calculation of survival rates for cancer. Proceedings of Staff Meeting Mayo Clinic 25, 270-286 DYSONJ.L., BEILBY J.O.W. & STEELE S.J. (1971) Factors influencing survival in carcinoma of the ovary. British Journal of Cancer 25, 237-249 EDERER F. (1961) A parametric estimate of the standard error of the survival rate. Journal of the American Statistics Association 56, I I 1-1 I 8 GREENWOOD M. (1926) The Natural Duration of Cancer. Report on the Public Health of Medical Subjects No. 33. HMSO, London HERTIGA.T. & GOREH. (1961) Tumors of the Female Sex Organs. At/as of Turnour Pathology. Section IX. Fascicle 3313. Armed Forces Institute of Pathology, Washington MALLOYJ.J., DOCKERTY M.B., WELCHJ.S. & HUNTA.B. (1965) Papillary ovarian tumors. I. Benign tumors and serous and mucinous cystadenocarcinomas. American Journal of Obstetrics and Gynecology 93, 867-879 MUNNELLE.W. & TAYLORH.C. (1949) Ovarian carcinoma: a review of 200 primary and 51 secondary cases. American Journal of Obstetrics Gynecology 58,943-959 SANTESSON L. & KOTTMEIER H.L. (1968) General classification of ovarian tumours. In U.I.C.C. Monograph I I , Ovarian Cancer, (eds F.Genti1 & A.C.Junqueira. Springer, Berlin SEALH.L. (1964) Multivariate statistical analysis for biologists. Methuen, London SEROVS.F. & SCULLYR.E. (1973) Histological typing of ovarian tumours. International Histological Classifications of Tumours, No. 9. WHO, Geneva TAYLORH.C. & LONGM.E. (1955) Problems of cellular and tissue differentiation in papillary adenocarcinoma of the ovary. American Journal of Obstetrics and Gynecology 70,753-765

Histological discrimination of malignancy in mucinous ovarian tumours.

Histopathology 1977, I, 431-443 Histological discrimination of malignancy in rnucinous ovarian tumours A . A G R O F O J 0 BL ANC O, A . C . C . G I...
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