{"id":"W4362541147","doi":"10.1158/1538-7445.am2023-5618","title":"Abstract 5618: Multi-institutional validation of a radiomics-based artificial intelligence method for predicting response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV NSCLC","year":2023,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Radiomics; Medicine; Response Evaluation Criteria in Solid Tumors; Oncology; Internal medicine; Stage (stratigraphy); Immunohistochemistry; Radiology; Clinical trial; Phases of clinical research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003718821,0.0009737372,0.0007709331,0.0008915922,0.0003324604,0.0007287185,0.001277455,0.001059905,0.0007356451],"category_scores_gemma":[0.004625514,0.0002598959,0.001164768,0.0005803735,0.000583376,0.0003183165,0.0006897943,0.0008219437,0.0004142249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009922571,"about_ca_system_score_gemma":0.000969503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01289155,"about_ca_topic_score_gemma":0.008232289,"domain_scores_codex":[0.9987811,0.0006042562,0.00009316803,0.0003290872,0.000122025,0.00007024004],"domain_scores_gemma":[0.997341,0.001088185,0.0003372751,0.0005305272,0.0004631959,0.0002397915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003927574,0.003673073,0.2874928,0.0004133866,0.002223226,0.0007423973,0.0001605624,0.584724,0.009546117,0.0003856652,0.01393293,0.09277822],"study_design_scores_gemma":[0.0002949727,0.001589307,0.09548745,0.00004064491,0.00024148,0.0002745106,0.0001195122,0.8937421,0.006237763,0.0003716694,0.001543703,0.00005694703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845898,0.0003513886,0.008301052,0.0002788198,0.00008521449,0.0002179085,0.004879076,0.0006041134,0.0006926868],"genre_scores_gemma":[0.9819993,0.00006788966,0.005693838,0.00008559709,0.00003269394,0.0001146739,0.01159437,0.00001722825,0.0003945305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01289155,"threshold_uncertainty_score":0.02563304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1582893837307817,"score_gpt":0.488269195823153,"score_spread":0.3299798120923714,"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."}}