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Abstract
Introduction/Background To improve epithelial ovarian cancer histotyping accuracy, Köbel et al. developed decision-tree algorithms based on immunohistochemistry. These algorithms included a six-split and a four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. This study evaluated the efficacy of these algorithms.
Methodology A gynaecological pathologist, blinded for the original diagnosis, first determined the haematoxylin and eosin-stained (H&E)-based histotype of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E sections in combination with the immunohistochemical stainings. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, PR, TFF3, and WT1 were scored. The algorithmic predictions were compared with the final histotypes to assess the accuracy, for which the early- and advanced stage algorithms were assessed together as the six-split-stages algorithm.
Results The six-split algorithm demonstrated 96.1% accuracy, while the six-split-stages and four-split algorithms both showed 93.5% accuracy. Of the 230 cases, 16 (7%) showed discordant original and final diagnoses; the algorithms concurred with the final diagnosis in 14/16 cases (87.5%). Moreover, in 12.4%-13.3% of the cases, the H&E-based histotype was changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%) compared to the other two algorithms, while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7–97.6%).
Conclusion Considering the higher sensitivity of the six-split algorithm for endometrioid carcinoma compared to the four-split algorithm, and a higher sensitivity for low-grade serous carcinoma compared to the six-split-stages algorithm, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice.
Disclosures The authors have no disclosures to declare that are relevant to the content of this study.
