Artificial intelligence analysis applied to the treatment of granulosa cell tumors of the ovary

Introduction Granulosa cell tumors (GCTs) of the ovary are rare malignancies with limited systemic treatment options and high recurrence rates. Combining tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-producing oncolytic viruses with procaspase-3 activator (PAC-1) presents a promising therapeutic strategy, as TRAIL initiates apoptosis while PAC-1 amplifies caspase activity. However, patient responses remain variable, necessitating predictive frameworks that can integrate biological complexity with clinical data. Methods We developed a hybrid framework that integrates a mechanistic mathematical model of TRAIL-oncolytic virus and PAC-1 therapy with machine learning (ML) algorithms to predict tumor dynamics in GCTs. Four datasets (continuous and categorical tumor size measurements) were analyzed. Clinical and imaging data were merged with individualized solutions from the mathematical model to generate enriched feature sets for ML training. Linear regression and neural network models were trained and evaluated using accuracy, F1 scores, and root mean