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Objective: To study the potential of nuclear morphometry in supporting the interpretation of fine needle aspiration biopsy (FNAB) samples of the breast fixed in 50% ethanol and centrifuged on slides., Study Design: Computerized morphometry was used to outline the nuclei of breast epithelial cells in breast cancer, fibroadenoma and fibrocystic disease. The diagnoses were histologically confirmed. We applied 2 different sampling methods (measurements done on cell groups and on free cells)., Results: The mean nuclear area of cell groups of malignant samples (23) varied from 42 to 125 microns 2, in fibroadenomas from 30 to 50 microns 2 and in fibrocystic disease from 26 to 57 microns 2. The mean nuclear area of free cells varied as follows: cancer, 66-181 microns 2; fibroadenoma, 33-70 microns 2; fibrocystic disease, 35-60 microns 2. Apocrine metaplasia was excluded from comparison on a morphologic basis., Conclusion: The study suggests that if the mean nuclear area of cell groups is < 42 microns 2, the lesion is probably benign; if > 57 microns 2, and apocrine metaplasia is excluded, malignancy should be considered. The differential diagnosis between carcinoma and fibroadenoma could be based on free cells: mean area of free cell nuclei < or = 65 microns 2 suggested a benign lesion, and of > or = 71 microns 2 suggested a malignant lesion. Morphometric nuclear size features (exemplified by nuclear area) appeared efficient in distinguishing between malignant and benign lesions when measured from free cells and cell groups.

Objective: To quantify the histologic changes caused by freezing during tissue processing and their influence on histologic malignancy grading as a prognostic factor in invasive ductal breast cancer., Study Design: We studied frozen and nonfrozen formalin-fixed, paraffin-embedded samples of 18 cases of invasive ductal breast cancer. Features associated with histologic malignancy grading of breast cancer--i.e., nuclear pleomorphism, mitotic index and tubular differentiation--were assessed by quantitative morphometric methods., Results: In our material, frozen samples consistently had a smaller mean nuclear profile area than nonfrozen samples (mean difference, 32%). Frozen nuclei were also clearly less symmetric and uniform in shape than non-frozen nuclei. Moreover, frozen samples had consistently higher mitotic indices than nonfrozen samples (mean difference, 66%, with the standardized mitotic index). Tubular differentiation, as expressed in fraction of fields with tubular differentiation, increased by 16% as a result of sample freezing., Conclusion: According to our results of morphometric measurement in invasive ductal breast cancer, great caution should be exercised when prognostic conclusions are based on frozen tissue samples.

Objective: To evaluate apoptotic activity in breast cancer from Nigerian (n = 300) and Finnish (n=285) women., Study Design: Apoptotic bodies were expressed as square millimeters of neoplastic tissue (apoptotic index [AI]). The standardized mitotic index (SMI) and mitotic activity index (MAI) estimated proliferation., Results: The mean (+/- SD) AI was higher in Nigeria (9.6+/-14.8/mm2) than in Finland (5.2+/-6.1/ mm2). In both populations, AI values were higher in premenopausal than postmenopausal women, in lymph node positive than lymph node negative tumors and in larger than smaller tumors. However, the differences were not statistically significant. Increasing histologic grade was associated with increasing AI values (Nigeria, P =.012; Finland, P= .0001). AI in infiltrating ductal carcinomas were higher than in special types of breast cancer (Nigeria, P = .0700; Finland, P = .0168). As a continuous variable, AI was a significant prognosticator (Nigeria, P = .0125, Finland, P = .0466). Increasing AI appeared to be associated with tumor progression and dedifferentiation. The higher SMI/AI in Nigeria (9.2) than in Finland (4.5) reflects higher proliferative activity in the Nigerian material. In multivariate analysis of AI, SMI, MAI and tumor size, the proliferative indices (SMI and MAI) and tumor size only were significant independent prognosticators., Conclusion: In Nigerian and Finnish material, AI has limited prognostic value as a tool in grading breast cancer. The higher mean SMI/AI in Nigerian cancer suggests a shift in the proliferation/cell death balance, which may be associated with a later phase of the cancer progression cascade.

Objective: To find efficient morphometric nucleolar features for distinguishing different prostatic atypia groups in fine needle aspiration biopsies., Study Design: A computerized interactive morphometry program was used to outline nucleoli of prostate cells. The samples were divided into three groups: group 1 (definitely benign [n = 10] and atypical but benign [n = 13]), group 2 (moderately atypical [n = 11] or highly suspicious [n = 13]) and group 3 (definitely malignant [n = 17])., Results: The analysis revealed a difference in the number of nucleoli between definitely benign samples and other atypia groups but not between the latter. Nucleolar size features were best in distinguishing between atypia groups. The sample-associated mean size features were more powerful than cell group-associated size features., Conclusion: The sample-associated mean area, defined from cells selected by an experienced cytologist, if larger than 2 micron 2, was strongly associated with definitely malignant samples. The same was true for the largest nucleolar area if larger than 5.0 micron 2. Morphometric nucleolar size features appeared efficient in distinguishing between definitely malignant and other samples.

Objective: To study the potential of nuclear size and shape estimates in interpreting fine needle aspiration biopsy (FNAB) samples of the prostate., Study Design: Morphometry was used to outline nuclei of prostate cells. Cell groups were selected by an experienced cytologist., Results: The mean area of nuclei in the most atypical cell groups among definitely malignant samples (n = 17) varied from 26.3 to 93.3 micron 2 and in normal prostate cells (n = 10) from 15.6 to 33.7 micron 2. Perfect distinction of definitely benign and slightly atypical samples (n = 13) from definitely malignant samples was possible when the samples were characterized by the weighted means of the mean nuclear areas of the cell groups in the samples. The means of individual cell groups allowed correct distinction in only 84.8% of cell groups. Shape factors did not have any diagnostic value., Conclusion: Morphometric nuclear size estimates from ethanol-fixed FNAB samples of the prostate are of diagnostic value and can potentially be used as part of multivariate diagnostic models when selected by an experienced cytologist according to strict criteria. However, measurement should be done from several cell groups (at least three of the most-atypical cell groups) in each sample.

Objective: To develop the necessary correction for the polymerase chain reaction-based oncogene amplification test applied to paraffin-embedded tissue sections., Study Design: The theoretical basis for making the correction in test results and for determining the neoplastic cell fraction was clarified, and a method for microscopic sampling was developed. The cell count bias was corrected by applying an equation by Ebbeson and Tang and using two adjacent tissue sections of different thickness, cut from the block from which DNA was isolated., Results: There was no need to determine the absolute thicknesses of the sections. Section thickness, as a relevant feature, was cancelled out in developing the formula for estimating the cell fraction. The correction was applied in practice, and when tested on 10 breast cancer samples, the correlation between amplification estimates from two observers was good (r = .96)., Conclusion: This method is valuable for interpreting the test results based on paraffin sections and for developing simpler quantitating methods for special test applications. The study also showed that amplifications may be more common than expected on the basis of the crude results of this type of test.

Objective: To evaluate the formula of Ebbeson and Tang (FET) with respect to the disector (DS) principle., Study Design: The DS principle has been proposed for avoiding cell count bias. DS is a slice of tissue, and from it those cells by area are counted in microscopy; the cells are not in contact with one of the surfaces of the slice. The resulting number divided by the thickness of the DS gives an accurate figure for cell number by volume. FET applies two sections of different thickness, usually cut adjacent. Cells seen in the sections are counted by area, the figures are subtracted from each other, and the difference is divided by the difference between the thicknesses of the sections. The result is cell number by volume: NV = (NA1 - NA2)/(t1 - t2)., Results: FET and the DS principle superficially appear different. However, from a geometric point of view they are based on the same principle. When the thickness of the thinner section of FET approaches zero, the situation is in all respects equal to the DS principle. The formula for DS can thus be written: NV = (NA1 - NA2)/t1., Conclusion: The result proves that in principle DS and FET are equivalent methods of counting cell numbers by volume in tissues. FET may be more easily applied in histopathology practice because visual comparison of the sections is not necessary. Section thickness, however, has to be measured from vertically embedded sections or with scanning laser confocal microscopy. FET shares the stereologically unbiased character of the DS principle and is independent of the size and shape of structures counted.

The reproducibility in quantitation of proliferation activity, determined using the monoclonal antibody 19A2 to proliferating cell nuclear antigen (PCNA), was tested in visual and computer-assisted analyses of brain tumor material. The PCNA labeling index was scored using count (PCNA-LI, visual and computer analyses) and area (PCNA-LIa, computer analysis) estimates of immunopositivity. The quality of immunostaining was the most important reason for variation in the assessment results. Other significant variation sources in the assessment were experience in selecting microscopic fields and distinguishing immunopositive nuclei from immunonegative ones. Computer-assisted analysis improved the reproducibility of quantitation between different observers (visual rPCNA-LI = 0.624 versus computer assisted rPCNA-LI = 0.904). Also, the use of PCNA-LIa improved the intraobserver and interobserver reproducibility in different stainings (observer 1:rPCNA-LI = 0.857 versus rPCNA-LIa = 0.874; observers 1 and 2: rPCNA-LI = 0.904 versus rPCNA-LIa = 0.927; observers 3 and 4: rPCNA-LI = 0.848 versus rPCNA-LIa = 0.906). PCNA-LIa by computerized image analysis improves accuracy in the evaluation of the granularly expressed PCNA level. Furthermore, the effect of tumor heterogeneity on the assessment results can be diminished with the computerized method because large tissue areas can be analyzed faster.

Four histologic staining methods used for detecting amyloid (Congo red, viewed in both normal and polarized light, Sirius red, Crystal violet and Thioflavine T) were applied to heart muscle autopsy samples from 19 patients who suffered from amyloidosis. The amount of amyloid present was evaluated with morphometry (point counting) by five pathologists, and the interobserver reproducibility and variation of point counting in these staining methods were analyzed. The Sirius red method showed the least variation and was the most suitable stain for demonstrating amyloid with respect to reproducibility. Thioflavine T showed the greatest variation and was the least suitable stain with respect to reproducibility. The range of variation was considerable in all staining methods. The results show that stains differ in their specificity and sensitivity in staining amyloid, observers differ in their interpretation of staining results and certain stains result in more uniform interpretations than do others.

The role of stereology and morphometry (including image analysis) in histopathology is considered. Diagnostic histopathology has its own character, which should be considered when quantitative methods are applied. Three different types of studies in histopathology can be distinguished: prospective, retrospective and routine diagnostic studies. The variation in these studies is due to differences in the methods and differences in how the methods are applied. The overall variation is smallest in prospective studies and largest in routine diagnostic studies. Statistical classification of the overall variation does not cover all relevant aspects of the situation in which we study the sample from one individual patient. In that situation, we do not get support from the samples of other patients. In fact, we should speak about group morphometry (statistical morphometry) when samples from several patients are studied and diagnostic morphometry when the sample from one individual patient is under investigation. In the former case, simple morphometric methods may be applicable (the researcher is interested in mean values). In the latter case, one would often like to analyze the sample in more detail. This is why the diagnostic situation may benefit from computerized image analysis, which allows collection of large amounts of data in a short time. On the other hand, the great variation between the pathologic and the normal can allow the use of simple methods. The above basic principles need be considered before morphometric and stereologic methods are applied. These methods are simple in practice and they should be included in the training of all pathologists.

Using morphometric methods, five pathologists analyzed the positive staining for carcinoembryonic antigen (CEA) in sections from 17 ovarian tumors, with the intraclass correlation coefficient (ICC) and the mean values of the coefficients of variation (CV) used to assess reproducibility and variation. First, field and point scores for epithelium and mucin were estimated using 25 randomly selected square fields in sections from each of the tumors. The ICC range in the whole sample field was 0.53 to 0.81 (slight to substantial reproducibility) while the mean values of CV were 0.50 to 0.75. Second, the results of using random and standardized individual fields for the measurements were studied in three tumors. In random fields, the ICC was 0.57 to 0.71 (slight to moderate reproducibility) and the mean values of CV were 0.53 to 0.65. The corresponding values in standardized fields were 0.71 to 0.73 (moderate reproducibility) and 0.41 to 0.57, respectively. The results show that the variation is smaller and the degree of reproducibility higher in standardized fields. Considerable variation remains, however, revealing human factors as an important source of variation in practical morphometry.

The application of morphometry in tumor pathology is discussed, e.g., its use in studying the biology of tumors, in creating tumor classification(s), in creating methods for the identification of a tumor in the diagnostic context, and in characterizing diagnostic histopathology in absolute terms. In traditional subjective diagnostic histopathology, reproducibility can be defined satisfactorily, but the definition of accuracy is ambiguous; in morphometric histopathology, a satisfactory definition is found for both concepts but it may be difficult to separate them in practice. Morphometric histopathology can study parameters measured from sections or parameters derived from the primary measurements through calculations. In the histopathology of tumors, the following parameters have turned out to be specially valuable: densitometric measurements of nuclei, nuclear area, perimeter and form factors, nucleolar parameters, the number of mitotic cells per area, the cellularity, the volume fraction of the epithelium, and parameters associated with the fraction of tumor tissue in the sample. The standard deviation or other moments of the distribution of these measurements can be more relevant than the mean values of the results. This indicates that more attention should be given to sampling rules, which are important in defining the efficiency of the methods. For rational application of morphometric methods, it is very important to make a distinction between group morphometry and diagnostic morphometry. The latter engenders numerous sources of variation (variation in section thickness, variation in tissue processing, variation in the techniques of measurement, interobserver variation, interlaboratory variation, variation due to subjective interpretation, etc.), which are usually better controlled in group morphometry. The influence on morphometric parameters of variation in section thickness and tissue shrinkage during processing are discussed.