{"id":"W4413560361","doi":"10.1200/cci-25-00105","title":"Machine Learning Model Integrating Computed Tomography Image–Derived Radiomics and Circulating miRNAs to Predict Residual Teratoma in Metastatic Nonseminoma Testicular Cancer","year":2025,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Testicular diseases and treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC; BC Cancer Agency","funders":"","keywords":"Medicine; Teratoma; Radiology; Univariate analysis; Univariate; Germ cell tumors; Pathology; Multivariate analysis; Internal medicine; Chemotherapy; Machine learning; Multivariate statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004239208,0.000322372,0.0007764909,0.0003219541,0.0001440291,0.0001063329,0.0001274774,0.0001170574,0.00001845332],"category_scores_gemma":[0.001483908,0.0002695995,0.0001755264,0.0006751878,0.0001322677,0.0001940443,0.0001664192,0.0005866752,0.000002765881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001803537,"about_ca_system_score_gemma":0.0005286744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004618064,"about_ca_topic_score_gemma":0.0001992892,"domain_scores_codex":[0.9974545,0.00009402724,0.001459189,0.0002929786,0.0003027295,0.0003966086],"domain_scores_gemma":[0.9981313,0.0007030878,0.000333714,0.0002882967,0.00020265,0.000340961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002841738,0.0002723719,0.9364706,0.0007238314,0.0004645,0.00007312607,0.001015803,0.0165569,0.0001650392,0.00007801809,0.0002060988,0.0436895],"study_design_scores_gemma":[0.003988586,0.0002347806,0.2205558,0.00155662,0.0006658962,0.00002003715,0.0002558351,0.7721471,0.00007689621,0.0002147336,0.00006075165,0.0002229629],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98424,0.002768461,0.01059989,0.0007807207,0.0001492347,0.0009808348,0.0001421307,0.00009888365,0.0002398362],"genre_scores_gemma":[0.921491,0.0002762864,0.0762958,0.001544281,0.00006138263,0.0001603442,0.00009696835,0.00002810524,0.00004579768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7555902,"threshold_uncertainty_score":0.9999756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03477870151027251,"score_gpt":0.3754711581399632,"score_spread":0.3406924566296907,"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."}}