{"id":"W4416713856","doi":"10.1016/j.crad.2025.107192","title":"An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study","year":2025,"lang":"en","type":"article","venue":"Clinical Radiology","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visualization; Computed tomography; Selection (genetic algorithm); Tomography; Artery; Helical computed tomography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001009238,0.0003080019,0.001058127,0.0006861023,0.0001142658,0.00004152548,0.0001890856,0.0002852715,0.00001984065],"category_scores_gemma":[0.00007418804,0.0002669645,0.0004027546,0.0007873933,0.0002133283,0.0001400541,0.00006558135,0.0002898461,0.000003926368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005716923,"about_ca_system_score_gemma":0.0001096697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003000639,"about_ca_topic_score_gemma":0.00001162471,"domain_scores_codex":[0.9969854,0.0004785784,0.001182056,0.0007897342,0.0001344856,0.0004297171],"domain_scores_gemma":[0.9980755,0.000983723,0.0001921707,0.0003687248,0.0001723043,0.0002075587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001187582,0.00335231,0.948373,0.00006464919,0.0003948758,0.000258463,0.0001916048,0.00003750255,0.0001093767,0.00009096038,0.0003452188,0.04559443],"study_design_scores_gemma":[0.005974885,0.01945051,0.5641666,0.0001218332,0.0003134327,0.00003167911,0.0001156246,0.4094377,0.00004974715,0.00006674676,0.0001071309,0.0001641152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8859548,0.0002156484,0.1102239,0.0001311084,0.0006330285,0.002681778,0.00001530236,0.0001255624,0.00001882687],"genre_scores_gemma":[0.9784999,0.00002650491,0.01984171,0.0005978925,0.0003069554,0.000262414,0.0004282274,0.00002819887,0.000008195964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4094002,"threshold_uncertainty_score":0.9999782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05513176236703098,"score_gpt":0.3797720524137925,"score_spread":0.3246402900467615,"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."}}