{"id":"W4412163754","doi":"10.1158/1557-3265.aimachine-b010","title":"Abstract B010: From RECIST to reality: A foundation model pipeline for scalable therapy response evaluation","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Structural Genomics Consortium; University Health Network","funders":"","keywords":"Pipeline (software); Medicine; Foundation (evidence); Response Evaluation Criteria in Solid Tumors; Medical physics; Computer science; Oncology; Intensive care medicine; Internal medicine; Chemotherapy; Progressive disease; Programming language; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001909552,0.001380189,0.0008060149,0.000901814,0.0005407888,0.002103456,0.002280589,0.001293341,0.01344981],"category_scores_gemma":[0.006177798,0.0008200723,0.00169653,0.0004782325,0.0006168563,0.001187643,0.002109028,0.002127303,0.005032058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525113,"about_ca_system_score_gemma":0.002426059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01398901,"about_ca_topic_score_gemma":0.01703496,"domain_scores_codex":[0.999385,0.0001434123,0.00003362814,0.0001900131,0.0001920592,0.0000558826],"domain_scores_gemma":[0.9985809,0.0006525676,0.0001022421,0.0002279874,0.000343101,0.00009314954],"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":[0.0007877363,0.0001769023,0.003944668,0.0003566435,0.0002473704,0.0003010149,0.000180265,0.6444858,0.008562103,0.01586821,0.05234176,0.2727475],"study_design_scores_gemma":[0.00002075719,0.00002123636,0.0001226372,0.00001242402,0.000007602158,0.00002195438,0.00000838467,0.9902277,0.00142531,0.005210961,0.002913313,0.000007787115],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01022297,0.0003114966,0.9345247,0.0008626194,0.0000865234,0.0001983701,0.002379782,0.04814551,0.003268116],"genre_scores_gemma":[0.2993562,0.0004472277,0.6768748,0.001022392,0.0001131763,0.000633173,0.009307524,0.006084884,0.006160622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01398901,"threshold_uncertainty_score":0.04499412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5985100643909758,"score_gpt":0.6717496096403198,"score_spread":0.07323954524934406,"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."}}