Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer.
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<h4>Background</h4>Identifying high-risk patients for platinum resistance is critical for improving clinical management of ovarian cancer. We aimed to use automated image analysis of hematoxylin & eosin (H&E) stained sections to identify the association between microenvironmental composition and platinum-resistant recurrent ovarian cancer.<h4>Methods</h4>Ninety-one patients with ovarian cancer containing the data of automated image analysis for H&E histological sections were initially reviewed.<h4>Results</h4>Seventy-one patients with recurrent disease were finally identified. Among 30 patients with high stromal cell ratio, 60% of the patients had platinum-resistant recurrence, which was significantly higher than the rate in patients with low stromal cell ratio (9.80%, P < 0.001). Multivariate logistic regression analysis revealed elevated CA125 level after 3 cycles of chemotherapy (P < 0.001) and high stromal cell ratio (P = 0.002) were the negative predictors of platinum-resistant relapse. The area under the curve (AUC) of receiver operating characteristic (ROC) curves of the models for predicting platinum-resistant recurrence with stromal cell ratio, normalization of CA125 level, and the combination of two parameters were 0.78, 0.79, and 0.89 respectively.<h4>Conclusions</h4>Our results demonstrated stromal cell ratio based on automated image analysis may be a potential predictor for ovarian cancer patients at high risk of platinum-resistant recurrence, and it could improve the predictive value of model when combined with normalization of CA125 level after 3 cycles of chemotherapy.
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Neoplasm Recurrence, Local
Drug Resistance, Neoplasm
Image Processing, Computer-Assisted
Computational Pathology & Integrated Genomics
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BMC cancer, 2019, 19 (1), pp. 159 - ?