{"id":"W4412163732","doi":"10.1158/1557-3265.aimachine-b020","title":"Abstract B020: Automated classification of thymic epithelial tumors. A novel deep learning approach","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":"","funders":"","keywords":"Deep learning; Pathology; Medicine; Cancer; Cancer research; Artificial intelligence; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008982974,0.001119209,0.0006976613,0.001609395,0.0003814682,0.0009787561,0.001657654,0.00147381,0.002451842],"category_scores_gemma":[0.001178905,0.0004170789,0.0009454222,0.0009684976,0.0002736275,0.0008104467,0.001328402,0.001189069,0.001510173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009038351,"about_ca_system_score_gemma":0.001047162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008641457,"about_ca_topic_score_gemma":0.008548512,"domain_scores_codex":[0.99953,0.00006457807,0.00003084136,0.0001687692,0.0001079769,0.00009791992],"domain_scores_gemma":[0.9996291,0.00007732376,0.00003926148,0.0000545203,0.0001554544,0.00004427805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000424046,0.0004176422,0.007527642,0.0001727689,0.0001760437,0.0002034729,0.00007557357,0.1384815,0.0225521,0.001723213,0.02390675,0.8043393],"study_design_scores_gemma":[0.00001610358,0.00005507183,0.00119982,0.00001244889,0.00001943177,0.00006206856,0.00001816911,0.9919012,0.004061091,0.001317658,0.001328121,0.000008872382],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2296368,0.002594926,0.7411769,0.001504183,0.0003607661,0.0003658136,0.004667641,0.01268844,0.007004587],"genre_scores_gemma":[0.7355955,0.0006634782,0.2342937,0.0009057595,0.0002480507,0.0003478107,0.01270209,0.0003195189,0.01492414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008641457,"threshold_uncertainty_score":0.01718229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1413354508483994,"score_gpt":0.5147644851405606,"score_spread":0.3734290342921612,"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."}}