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