{"id":"W2549097358","doi":"10.15353/vsnl.v1i1.46","title":"Discovery Radiomics for Imaging-driven Quantitative Personalized Cancer Decision Support","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Radiomics; Feature (linguistics); Cancer; Lung cancer; Prostate cancer; Computer science; Medicine; Artificial intelligence; Oncology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005211622,0.0001884042,0.0003731581,0.0001797521,0.00009231955,0.00008852849,0.0001420054,0.00004641058,0.00005109595],"category_scores_gemma":[0.0006911916,0.0001488636,0.0001899331,0.0001590871,0.0001971194,0.0002803523,0.00005129507,0.0002575153,0.00002700869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018482,"about_ca_system_score_gemma":0.0002206215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008050015,"about_ca_topic_score_gemma":0.000002108502,"domain_scores_codex":[0.9984685,0.00004409224,0.0003041686,0.0003902121,0.0004596631,0.0003334077],"domain_scores_gemma":[0.9989487,0.0002818721,0.0001224629,0.0002343758,0.0001278322,0.0002847516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002646323,0.0002037822,0.03106339,0.0001235336,0.0001831374,0.0002188485,0.002482729,0.001491372,0.09840319,0.001774558,0.7896054,0.07180375],"study_design_scores_gemma":[0.02111473,0.0009252349,0.009377195,0.0008319683,0.0004409121,0.000249032,0.0009781728,0.3888264,0.001201593,0.0006270158,0.5746279,0.0007998854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5404476,0.0004431487,0.399164,0.05788616,0.001163816,0.0005297096,0.00003165462,0.00008548034,0.0002484586],"genre_scores_gemma":[0.7177593,0.0002781777,0.2182686,0.06005168,0.001136087,0.0001534902,0.0002804242,0.0001803828,0.001891843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.387335,"threshold_uncertainty_score":0.6070482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02630702879928695,"score_gpt":0.3687616626528679,"score_spread":0.3424546338535809,"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."}}