Predicting efficacy in patients with locally advanced (LA)/metastatic urothelial carcinoma (mUC) treated with avelumab using machine learning and explainability approaches.
Authors: Giannatempo Patrizia , Miskovic Vanja , Piceni Matteo , Gambale Elisabetta , Stellato Marco , Bottiglieri Achille , Walter Ferrari Bravo , Oldani Simone , Maruzzo Marco , Bimbatti Davide , Mennitto Alessia , Rebuzzi Sara Elena , Mercinelli Chiara , Sorarù Mariella , Galli Luca , Messina Carlo , Iacovelli Roberto , Antonuzzo Lorenzo , Prelaj Arsela , Procopio Giuseppe 
Published: January 2024

688 Background: Internationally avelumab is approved as maintenance therapy for patients (pts) with LA/mUC whose disease did not progress after 1L platinum-based chemotherapy. However, 54% of pts progressed on avelumab. Limited data are available on predictive biomarker of efficacy. Artificial intelligence (AI) methods are being increasingly investigated to generate predictive models applicable in clinical practice. In this study, we developed a set of machine learning (ML) classifiers and survival analysis algorithms using real-world data to predict response and progression free survival (PFS) in LA/mUC patients treated with avelumab. We also applied explainability to the developed algorithms. Methods: We prospectively collected real-world data from 115 pts receiving Avelumab from 2021 to 2022 treated in 20 institutions in Italy (MALVA dataset). In order to predict the efficacy of immunotherapy (IO), 2 different outcomes were studied: Objective Response Rate (ORR) and Progression Free Survival (PFS). The dataset was split between training and test set, with a 80%-20% ratio.The missing values were imputed using a Bayesian Ridge iterative imputer, fitted on the training set. Eight different classifier models were used for ORR: XGBoost (XGB), Logistic Regression (LR), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Adaboost (AB), Extra Trees (ET) and LightGBM (LGBM). Five ML survival analysis models were used to analyse PFS: Cox Proportional Hazards (CPH), Random Survival Forest (RSF), Gradient Boosting (GB), Extra Survival Trees (EST) and Survival Support Vector Machine (SSVM). Finally, SHAP values, an eXplainable AI (XAI) technique, were calculated to evaluate each feature and to explain the predictions. Results: According to clinical expertise, 31 features were selected through a clinical hypothesis. For ORR prediction, the two best performing models were XGB and ET, both without using oversampling. On the test set, XGB achieved a F1 score of 0.77, accuracy of 0.77 and AUC of 0.81, while ET reached F1 score and accuracy of 0.81 and AUC of 0.80. Regarding the prediction of PFS, EST and RSF obtained the best performances with a c-index of 0.71 and 0.72, and Average AUC of 0.75 and 0.76, respectively. According to SHAP, the most important feature for predicting ORR was: ORR after 1st line CHT, while bone metastases, absolute leukocytes number at baseline and ECOG PS were the most important features for the PFS prediction. Conclusions: Machine learning is useful to predict efficacy in advanced urothelial carcinoma. The explainability models confirmed what have been discovered within the last years of immune-research conferring trustworthiness to the ML models. Further validation of these approaches on larger and external pts cohorts is needed.