Department of Pathology, Cancer Center Amsterdam, Amsterdam Reproduction & Development Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, the Netherlands.
Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, the Netherlands.
Hum Pathol. 2024 Sep;151:105631. doi: 10.1016/j.humpath.2024.105631. Epub 2024 Jul 30.
To improve the precision of epithelial ovarian cancer histotyping, Köbel et al. (2016) developed immunohistochemical decision-tree algorithms. These included a six- and four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. In this study, we evaluated the efficacy of these algorithms. A gynecological pathologist determined the hematoxylin and eosin (H&E)-based histotypes of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E-stained sections and immunohistochemistry outcomes. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, progesterone receptor (PR), trefoil factor 3 (TFF3), and Wilms' tumor 1 (WT1) were scored. The algorithmic predictions were compared with the final histotypes to assess their precision, for which the early- and advanced stage algorithms were assessed together as six-split-stages algorithm. The six-split algorithm demonstrated 96.1% precision, whereas the six-split-stages and four-split algorithms showed 93.5% precision. 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%). In 12.4%-13.3% of cases, the H&E-based histotype changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%), while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7-97.6%). Considering the higher sensitivity of the six-split algorithm for endometrioid and low-grade serous carcinoma compared with the four-split and six-split-stages algorithms, respectively, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice.
为了提高上皮性卵巢癌组织学分型的准确性，Köbel 等人（2016 年）开发了免疫组织化学决策树算法。这些算法包括一个六分法和一个四分法，以及一个用于早期和晚期疾病的单独的六分法。在这项研究中，我们评估了这些算法的疗效。一名妇科病理学家根据苏木精和伊红（H&E）确定了 230 名患者的组织学类型。随后，通过重新评估 H&E 染色切片和免疫组织化学结果来确定最终的组织学类型。为了使用算法进行组织型预测，对 Napsin A、p16、p53、孕激素受体（PR）、三叶因子 3（TFF3）和维尔姆斯瘤 1（WT1）等免疫组织化学标志物进行了评分。将算法预测与最终组织学类型进行比较，以评估其准确性，将早期和晚期算法一起评估为六分法阶段算法。六分法算法的准确率为 96.1%，而六分法阶段和四分法算法的准确率为 93.5%。在 230 例病例中，有 16 例（7%）出现了原始和最终诊断不一致的情况；在 16 例中，有 14 例（87.5%）与最终诊断一致。在 12.4%-13.3%的病例中，基于算法结果，H&E 组织学类型发生了变化。对于低级别浆液性癌，六分法阶段算法的敏感性较低（80%对 100%），而四分法阶段算法对子宫内膜样癌的敏感性较低（78%对 92.7%-97.6%）。考虑到六分法算法对子宫内膜样癌和低级别浆液性癌的敏感性高于四分法和六分法阶段算法，我们建议在临床实践中采用六分法算法对上皮性卵巢癌进行组织学分型。
