Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

Abstract

Simple Summary High-grade serous ovarian carcinoma (HGSOC) is the most aggressive histologic type of epithelial ovarian cancer, associated with high recurrence and mortality rates despite standard treatment. In accordance with the era of precision cancer medicine, we aimed to develop machine learning models predicting platinum sensitivity in patients with HGSOC. First, we collected patients' clinicopathologic data from three tertiary hospitals. Second, we elected six variables associated with platinum sensitivity using the stepwise selection method. Third, based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network. Evaluation of model performance with the five-fold cross-validation method identified the LR-based model as the best at identifying platinum-resistant cases. Lastly, we developed a web-based nomogram by fitting the LR model results for clinical utility. Based on the prediction results, physicians may implement individualized treatment and surveillance plans for each HGSOC patient. To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients' clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.