ArticleBMC cancer2019
Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- Spatiotemporal architecture of immune cells and cancer-associated fibroblasts in high-grade serous ovarian carcinoma.Science advances · 2024Article
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
- Knockdown of MUC16 (CA125) Enhances the Migration and Invasion of Hepatocellular Carcinoma Cells.Frontiers in oncology · 2021Article
- Computational analysis of morphological and molecular features in gastric cancer tissues.Cancer medicine · 2020Article
- Recent advances in tissue imaging for cancer research.F1000Research · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundIdentifying 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.
methodsNinety-one patients with ovarian cancer containing the data of automated image analysis for H&E histological sections were initially reviewed.
resultsSeventy-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.
conclusionsOur 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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