{"id":"W2397554324","doi":"10.1158/1538-7445.sabcs15-p1-01-04","title":"Abstract P1-01-04: Early stage breast cancer prognostication using whole tumor or Ki67 heterogeneity-based digital imaging","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Roche (Canada)","funders":"","keywords":"Medicine; Breast cancer; Immunohistochemistry; Stage (stratigraphy); Cohort; Oncology; Cancer; Internal medicine; Adjuvant therapy; Pathology; Radiology; Biology","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":[0.00217801,0.0003327057,0.0002738595,0.0007585457,0.0001827339,0.0007452335,0.0005136846,0.0002666746,0.005796918],"category_scores_gemma":[0.00247651,0.0001528725,0.0002776183,0.0006634605,0.0003685703,0.000326158,0.0004929947,0.0003407367,0.001111065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003972639,"about_ca_system_score_gemma":0.0006279554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009430921,"about_ca_topic_score_gemma":0.00102657,"domain_scores_codex":[0.999577,0.0001270392,0.00004069702,0.00008974772,0.0001316938,0.0000337676],"domain_scores_gemma":[0.9989497,0.0002770835,0.0002534178,0.000189497,0.0002426388,0.00008759274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006979465,0.0004293422,0.654221,0.0006960281,0.0003462921,0.0004451068,0.0001418005,0.005385132,0.09915043,0.0008163482,0.008231583,0.2231575],"study_design_scores_gemma":[0.0004489913,0.001868839,0.9037766,0.00005773863,0.0003520739,0.001669946,0.0001097989,0.01253225,0.06809464,0.001379501,0.009666708,0.00004284592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591278,0.001491993,0.02678142,0.000376298,0.00006928673,0.0008173059,0.004794591,0.0004786352,0.006062622],"genre_scores_gemma":[0.9768152,0.0004156108,0.0151198,0.00007264115,0.0000583835,0.0003841258,0.004355689,0.0000732106,0.002705345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005796918,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06494183270319297,"score_gpt":0.4237404831507792,"score_spread":0.3587986504475862,"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."}}