<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>12</volume><submitter>Prelaj A</submitter><pubmed_abstract>&lt;h4>Introduction&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 Fa</pubmed_abstract><journal>Frontiers in oncology</journal><pagination>1078822</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9899835</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.</pubmed_title><pmcid>PMC9899835</pmcid><pubmed_authors>Zanitti M</pubmed_authors><pubmed_authors>de Braud FGM</pubmed_authors><pubmed_authors>Pedrocchi ALG</pubmed_authors><pubmed_authors>Garassino MC</pubmed_authors><pubmed_authors>Mazzeo L</pubmed_authors><pubmed_authors>Bottiglieri A</pubmed_authors><pubmed_authors>Spagnoletti A</pubmed_authors><pubmed_authors>Signorelli D</pubmed_authors><pubmed_authors>Pesenti M</pubmed_authors><pubmed_authors>Rametta A</pubmed_authors><pubmed_authors>Prelaj A</pubmed_authors><pubmed_authors>Galli G</pubmed_authors><pubmed_authors>Rinaldi A</pubmed_authors><pubmed_authors>Pedica B</pubmed_authors><pubmed_authors>Trovo F</pubmed_authors><pubmed_authors>Marinacci R</pubmed_authors><pubmed_authors>Pircher CC</pubmed_authors><pubmed_authors>De Toma A</pubmed_authors><pubmed_authors>Beninato T</pubmed_authors><pubmed_authors>Ganzinelli M</pubmed_authors><pubmed_authors>Proto C</pubmed_authors><pubmed_authors>Di Mauro MR</pubmed_authors><pubmed_authors>Brambilla M</pubmed_authors><pubmed_authors>Kosta S</pubmed_authors><pubmed_authors>Viscardi G</pubmed_authors><pubmed_authors>Ferrara R</pubmed_authors><pubmed_authors>Giani C</pubmed_authors><pubmed_authors>Galli EG</pubmed_authors><pubmed_authors>Miskovic V</pubmed_authors><pubmed_authors>Antonia M</pubmed_authors><pubmed_authors>Di Gregorio S</pubmed_authors><pubmed_authors>Lo Russo G</pubmed_authors><pubmed_authors>Provenzano L</pubmed_authors><pubmed_authors>Occhipinti M</pubmed_authors><pubmed_authors>Restelli M</pubmed_authors><pubmed_authors>Manglaviti S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.</name><description>&lt;h4>Introduction&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 Fa</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022</publication><modification>2025-04-04T12:56:23.205Z</modification><creation>2025-04-04T12:56:23.205Z</creation></dates><accession>S-EPMC9899835</accession><cross_references><pubmed>36755856</pubmed><doi>10.3389/fonc.2022.1078822</doi></cross_references></HashMap>