Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer

• Main Authors: C. Lan, J. Li, X. Huang, A. Heindl, Y. Wang, S. Yan, Y. Yuan
• Format: Article
• Language: English
• Published: BMC 2019-02-01
• Series: BMC Cancer
• Subjects: Automated image analysis; Stromal cell ratio; Ovarian cancer; Platinum resistant relapse
• Online Access: http://link.springer.com/article/10.1186/s12885-019-5343-8

Abstract Background 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. Methods Ninety-one patients with ovarian cancer containing the data of automated image analysis for H&E histological sections were initially reviewed. Results 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. Conclusions 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.