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Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients by Arsela Prelaj; Edoardo Gregorio Galli; Vanja Miskovic; Mattia Pesenti; Giuseppe Viscardi; Benedetta Pedica; Laura Mazzeo; Achille Bottiglieri; Leonardo Provenzano; Andrea Spagnoletti; Roberto Marinacci; Alessandro De Toma; Claudia Proto; Roberto Ferrara; Marta Brambilla; Mario Occhipinti; Sara Manglaviti; Giulia Galli; Diego Signorelli; Claudia Giani; Teresa Beninato; Chiara Carlotta Pircher; Alessandro Rametta; Sokol Kosta; Michele Zanitti; Maria Rosa Di Mauro; Arturo Rinaldi; Settimio Di Gregorio; Martinetti Antonia; Marina Chiara Garassino; Filippo G. M. de Braud; Marcello Restelli; Giuseppe Lo Russo; Monica Ganzinelli; Francesco Trovò; Alessandra Laura Giulia Pedrocchi is a Medicine article available to read on EtoBox.

What is Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients about?

## Introduction Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. ## Methods We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ## Results Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB perf

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Author

Arsela Prelaj; Edoardo Gregorio Galli; Vanja Miskovic; Mattia Pesenti; Giuseppe Viscardi; Benedetta Pedica; Laura Mazzeo; Achille Bottiglieri; Leonardo Provenzano; Andrea Spagnoletti; Roberto Marinacci; Alessandro De Toma; Claudia Proto; Roberto Ferrara; Marta Brambilla; Mario Occhipinti; Sara Manglaviti; Giulia Galli; Diego Signorelli; Claudia Giani; Teresa Beninato; Chiara Carlotta Pircher; Alessandro Rametta; Sokol Kosta; Michele Zanitti; Maria Rosa Di Mauro; Arturo Rinaldi; Settimio Di Gregorio; Martinetti Antonia; Marina Chiara Garassino; Filippo G. M. de Braud; Marcello Restelli; Giuseppe Lo Russo; Monica Ganzinelli; Francesco Trovò; Alessandra Laura Giulia Pedrocchi

Publisher

Frontiers Media SA

Published

2023

Language

EN

Field

Medicine (Health Sciences)