{"id":"W4412163844","doi":"10.1158/1557-3265.aimachine-a032","title":"Abstract A032: Using machine learning to tackle tumor heterogeneity","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tumor heterogeneity; Cancer; Medicine; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004482493,0.001526887,0.001590057,0.00242882,0.0006313595,0.001939966,0.002045002,0.001663802,0.002769853],"category_scores_gemma":[0.007467433,0.0005808212,0.002075309,0.001602525,0.0005429338,0.001443909,0.001702405,0.001819514,0.001264351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008353055,"about_ca_system_score_gemma":0.001208778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005327536,"about_ca_topic_score_gemma":0.00393423,"domain_scores_codex":[0.9982983,0.0007025603,0.00008579972,0.0004717183,0.0002730936,0.0001685288],"domain_scores_gemma":[0.9959654,0.002505287,0.0002749251,0.0004396525,0.0005973962,0.0002172838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000639758,0.0004038077,0.02567102,0.0003284141,0.0009312166,0.000383639,0.000105959,0.6949189,0.004402004,0.004113094,0.01771375,0.2503884],"study_design_scores_gemma":[0.00001664034,0.00003258527,0.0006064978,0.000007249896,0.00001742927,0.00002221462,0.000009046516,0.9944639,0.0003015343,0.003945647,0.0005700194,0.000007162958],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1599296,0.002895233,0.8154197,0.002328477,0.0005899749,0.0002791391,0.004048239,0.009993521,0.004516245],"genre_scores_gemma":[0.7530652,0.0004939025,0.2299637,0.0008569951,0.0005964338,0.0003284253,0.009375582,0.0008598546,0.004459997],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.005327536,"threshold_uncertainty_score":0.02370596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1972787579798199,"score_gpt":0.5708786982592107,"score_spread":0.3735999402793908,"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."}}