{"id":"W4388798703","doi":"10.1016/j.jtocrr.2023.100602","title":"Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study","year":2023,"lang":"en","type":"article","venue":"JTO Clinical and Research Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier de l'Université Laval; Université Laval; Centre Hospitalier de l’Université de Montréal; Centre hospitalier universitaire de Québec; Université du Québec à Trois-Rivières","funders":"Fonds de Recherche du Québec - Santé; Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval","keywords":"Pembrolizumab; Nivolumab; Radiomics; Concordance; Medicine; Oncology; Internal medicine; Cohort; Imaging biomarker; Biomarker; Radiology; Immunotherapy; Cancer; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005958839,0.0002271539,0.0006995673,0.0004019052,0.0002726597,0.00009761551,0.00006127124,0.0001030221,0.000004717738],"category_scores_gemma":[0.005122979,0.0001387904,0.00006628875,0.0005570812,0.00129312,0.00009542359,0.0001529236,0.000908062,0.000001173684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004043946,"about_ca_system_score_gemma":0.0004219484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008931589,"about_ca_topic_score_gemma":0.0002137888,"domain_scores_codex":[0.9964424,0.00037814,0.000748447,0.0008361451,0.001021644,0.000573213],"domain_scores_gemma":[0.9977918,0.0009424226,0.0001122645,0.0003035784,0.0002509081,0.0005990723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001200565,0.0008614472,0.9495331,0.000103619,0.0001395703,0.0086069,0.00009208655,0.000008917201,0.00006287565,0.000005627453,0.00007290964,0.03931235],"study_design_scores_gemma":[0.0130146,0.00111612,0.9590725,0.0002632711,0.00006034661,0.0003333586,0.0003691392,0.02494339,0.000008050064,0.00003830675,0.0006271721,0.0001537796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947504,0.0003433799,0.00006028856,0.00297737,0.00009846879,0.001454646,0.000003677361,0.0001258094,0.0001859907],"genre_scores_gemma":[0.9984725,0.0001711634,0.0006710879,0.00007490708,0.00005834859,0.0001283142,0.00003063086,0.00002927577,0.0003637755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03915857,"threshold_uncertainty_score":0.6133054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07396518050724483,"score_gpt":0.4467872464758412,"score_spread":0.3728220659685964,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}