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A study of the predictive value of morphometric assessments in clinical outcome in ovarian epithelial malignancy

By Julia Elizabeth Palmer


Quantitative pathology as a tool in gynaecological pathology is fairly new. Such techniques allow greater objectivity than histological grading, typing, and residual tumour estimation. This study aims to determine: whether basic morphometry data can predict outcome and chemotherapeutic response, whether newer semi-automated methods of tumour morphometry provide similar results to older methods, and whether advanced image analysis methods can offer further tumour outcome data in ovarian carcinoma. The study was performed on a well-selected group of serous ovarian carcinomas. Tumour outcome, survival and chemotherapeutic response, were investigated in 132 patients treated with the same platinum containing regimes. Traditional clinicopathologic parameters, p53 & Bcl2, mitotic activity index (MAn and angiogenesis determinants were initially investigated. Semi-automated analysis, using immunohistochemically based techniques, were applied to estimate volume percentage epithelium (VPE) and nuclear morphometric parameters. Syntactic structure analysis including, minimum spanning tree, and neighbourhood features, was also investigated. Multivariate analysis revealed residual disease status, FIGO stage, MAl, VPE, equivalent nuclear diameter, and angiogenesis parameters to be strong prognosticators for overall and disease free survival. Residual disease status, VPE, nuclear length and angiogenesis parameters were found significant predictors of chemotherapy response. Angiogenesis parameters, as determined by semi-automated image analysis techniques, were found overall to be the strongest prognosticators. Morphometric data can predict outcome and chemotherapeutic response in ovarian serous carcinoma. Semi-automated morphometry techniques provide similar results to ()lder methods, and advanced image analysis can offer further outcome data. The rationale for the application of semi-automated and automated detection is that it may provide an unbiased sampling of a lesion and possibly a more representative estimate of areas that a human expert might label. Such determined, quantitative pathological findings were found to have important value in predicting prognosis in ovarian carcinoma and, if not to supersede, certainly to add to classical prognostic factors

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