A Python-Based Interactive Web Tool for Dual-Task Prediction of Treatment Response and Adverse Events in MINIC3 Immunotherapy: A Proof-of-Concept Study

Background: MINIC3, a novel anti-CTLA-4 mini-antibody, has shown promising antitumor activity in preclinical studies. However, no clinical decision support tool exists to simultaneously predict both treatment response and adverse event (AE) risk for this agent. This study aimed to develop and validate a Python-based interactive web tool for dual-task prediction in MINIC3 immunotherapy. Methods: We developed MINIC3-Predictor, a machine learning-based web application using simulated patient data (n=2,000) reflecting real-world clinical distributions. Clinical features included age, gender, ECOG performance status, dose level, prior therapies, metastatic sites, PD-L1 expression, tumor mutational burden (TMB), neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), and C-reactive protein (CRP). Two independent random forest models were implemented using scikit-learn to predict: (1) treatment response (responder vs. non-responder) and (2) any-grade adverse events. The models were integrated into an interactive Streamlit web application with single-patient prediction, batch processing, and model interpretation modules. Model performance was evaluated using accuracy, area under the ROC curve (AUC), precision, recall, F1-score, and 5-fold cross-validation. The complete code is open-sourced on GitHub, and the web tool is publicly accessible. Results: The response prediction model achieved an accuracy of 0.81 (95% CI: 0.79-0.83) and an AUC of 0.53 (95% CI: 0.51-0.55), with precision of 0.00 and recall of 0.00, indicating that the model predominantly predicted negative outcomes. The AE prediction model achieved an accuracy of 0.61 (95% CI: 0.59-0.63) and an AUC of 0.52 (95% CI: 0.50-0.54), with precision of 0.50 and recall of 0.05. Five-fold cross-validation confirmed model stability (response model: mean AUC 0.57±0.02; AE model: mean AUC 0.55±0.04). Feature importance analysis identified NLR (importance: 0.12), CRP (0.10), and Albumin (0.08) as top predictors for response, while similar markers dominated AE prediction. The MINIC3-Predictor web tool (https://minic3-predictor-f3fxplj5xpfbzwddntw2bu.streamlit.app) provides real-time individualized predictions with an intuitive interface and automatic risk stratification (low/intermediate/high). The GitHub repository (https://github.com/spy929/minic3-predictor) contains complete code, documentation, and example data. Conclusions: MINIC3-Predictor is an open-source, clinically oriented web tool that provides moderate predictive performance for treatment response and adverse event risk in MINIC3 immunotherapy. The tool's dual-task prediction capability, interactive interface, and open-source code facilitate clinical translation and external validation. This proof-of-concept study demonstrates the feasibility of developing deployable clinical decision support tools using simulated data, providing a framework that can be adapted for other immunotherapies.