{"id":"W4406834106","doi":"10.1158/1557-3265.targetedtherap-p008","title":"Abstract P008: Evaluating associations between genomic classifier and digital pathology based mutli-modal AI biomarkers in oligometastatic castration-sensitive prostate cancer","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":"University of New Brunswick","funders":"","keywords":"Prostate cancer; Medicine; Classifier (UML); Pathology; Digital pathology; Cancer; Oncology; Internal medicine; Artificial intelligence; Computer science","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":[0.000688692,0.000256179,0.0002880388,0.001285319,0.0002526457,0.0008305455,0.0003373352,0.0002748008,0.002979279],"category_scores_gemma":[0.002807039,0.0001197023,0.0003118312,0.001224653,0.000240813,0.000380884,0.0005505589,0.0003415156,0.0004805115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002910286,"about_ca_system_score_gemma":0.0002721263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008468498,"about_ca_topic_score_gemma":0.001201298,"domain_scores_codex":[0.9995054,0.0001071832,0.00005641913,0.0001451701,0.0001391444,0.00004665219],"domain_scores_gemma":[0.9980922,0.0005270838,0.0007082262,0.0001242233,0.0003521751,0.0001960989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004061133,0.00003029399,0.9904782,0.00005224537,0.00008426328,0.00008298316,0.00002569703,0.0001274446,0.001450052,0.00002866247,0.0002451634,0.00698898],"study_design_scores_gemma":[0.00001252498,0.00043322,0.994471,0.00001516554,0.0001126874,0.0007762519,0.0001186674,0.001649726,0.001608629,0.00008094757,0.0007135696,0.000007750335],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965504,0.0005243601,0.0008015441,0.00004424046,0.00001099183,0.00003429515,0.001436117,0.00002318354,0.0005746871],"genre_scores_gemma":[0.9984483,0.00006170814,0.0005863086,0.00001461392,0.00001021906,0.00001615839,0.0006734356,0.00000621459,0.0001830983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002979279,"threshold_uncertainty_score":0.009966671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1536082938741808,"score_gpt":0.5487248457129404,"score_spread":0.3951165518387596,"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."}}