{"id":"W2990040492","doi":"10.1148/rycan.2019190031","title":"Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response","year":2019,"lang":"en","type":"review","venue":"Radiology Imaging Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Natural Science Foundation of China; Ministry of Science and Innovation, New Zealand","keywords":"Immunotherapy; Molecular imaging; Medicine; Cancer immunotherapy; Functional imaging; Cancer; Modalities; Biomarker; Oncology; Medical physics; Internal medicine; Radiology; Biology; In vivo","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":[0.00171353,0.001395674,0.002116933,0.004538363,0.0002153382,0.001651228,0.001034689,0.001842389,0.002119502],"category_scores_gemma":[0.002679236,0.0003700631,0.0008841584,0.002371794,0.0009040319,0.00146517,0.0007580341,0.002197385,0.002155141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000999553,"about_ca_system_score_gemma":0.0009629669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266779,"about_ca_topic_score_gemma":0.0009414258,"domain_scores_codex":[0.9992132,0.0002117494,0.0001020223,0.0001343486,0.0002946894,0.00004402573],"domain_scores_gemma":[0.9982187,0.0009953243,0.000218833,0.00005220068,0.000466804,0.00004810034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006465284,0.00009759654,0.001454292,0.008402555,0.0001785161,0.0001963575,0.0000493923,0.0007902256,0.003036826,0.005091544,0.01955037,0.9610876],"study_design_scores_gemma":[0.00003351496,0.00032357,0.007520047,0.01087097,0.000685402,0.006937277,0.0001819274,0.001965083,0.00625925,0.009059535,0.9560209,0.000142523],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002481368,0.9955448,0.001629834,0.0005744687,0.0002451798,0.00001910849,0.00006068152,0.00002590202,0.001651826],"genre_scores_gemma":[0.00514641,0.9886557,0.003648234,0.0006479261,0.0005500742,0.00006312007,0.000159517,0.00001013728,0.001118805],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004538363,"threshold_uncertainty_score":0.009062111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04577564204199543,"score_gpt":0.4388238504037026,"score_spread":0.3930482083617072,"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."}}