{"id":"W4387207589","doi":"10.1016/j.ijrobp.2023.06.404","title":"Post-Prostatectomy Risk Stratification of Biochemical Recurrence Using Transfer Learning-Based Multi-Modal Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Sinai Hospital; University Health Network; Princess Margaret Cancer Centre; University of Toronto","funders":"NRG Oncology; Pfizer; Veracyte; National Cancer Institute; National Institutes of Health; Bayer HealthCare; RefleXion Medical; Janssen Scientific Affairs","keywords":"Medicine; Prostatectomy; Prostate cancer; Biochemical recurrence; Prostate biopsy; Artificial intelligence; Digital pathology; Biopsy; Feature selection; Urology; Radiology; Pathology; Internal medicine; Cancer; 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":[0.001050121,0.0004673278,0.0005636085,0.001043548,0.0002371882,0.0007537248,0.000572856,0.0004844971,0.000938015],"category_scores_gemma":[0.002695693,0.0001170193,0.0007612566,0.0004262347,0.0001750939,0.0005447585,0.0006595361,0.0006948338,0.0002346892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003796288,"about_ca_system_score_gemma":0.0004438099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003764793,"about_ca_topic_score_gemma":0.003058166,"domain_scores_codex":[0.9996418,0.0001126246,0.00003071603,0.00009714024,0.00006290906,0.0000547796],"domain_scores_gemma":[0.9991683,0.0004837754,0.00009328124,0.0000398772,0.0001615918,0.00005325046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001419261,0.001298285,0.1460769,0.000146443,0.0005933901,0.0002680461,0.0002011979,0.2780098,0.008186628,0.0009135762,0.003398832,0.5594876],"study_design_scores_gemma":[0.00001250163,0.0001370936,0.01501054,0.00000968517,0.00005761708,0.00004802641,0.00003651647,0.9823568,0.001026027,0.001145634,0.0001424582,0.00001698642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7903188,0.0009147613,0.2045466,0.0005917269,0.00008827209,0.0001283996,0.0006208275,0.0005977874,0.002192736],"genre_scores_gemma":[0.9901521,0.00006689257,0.009055292,0.00004042802,0.00002253302,0.00002867055,0.0002977712,0.000009164444,0.0003271341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003764793,"threshold_uncertainty_score":0.007485747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05293553225542841,"score_gpt":0.3720282460488172,"score_spread":0.3190927137933888,"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."}}