{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001232909,0.0009298336,0.0007815843,0.001555974,0.0002708539,0.000811209,0.0006552691,0.0005204605,0.001002172],"category_scores_gemma":[0.001569626,0.0002019235,0.0009790857,0.0004340385,0.0002221788,0.0003029122,0.000348441,0.0005473678,0.0004201634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006704237,"about_ca_system_score_gemma":0.000958184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038381,"about_ca_topic_score_gemma":0.006160601,"domain_scores_codex":[0.9997154,0.00007105029,0.00002466754,0.00008975638,0.00005191526,0.00004716334],"domain_scores_gemma":[0.9994387,0.0002846432,0.0000876872,0.00002348031,0.0001210076,0.00004442025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001468738,0.0006856073,0.2594959,0.0001147117,0.0005768611,0.0004884083,0.00008581659,0.5313434,0.008062473,0.0004086809,0.003352852,0.1939167],"study_design_scores_gemma":[0.000009598633,0.00009513937,0.006066943,0.000009073618,0.00005165914,0.00006098369,0.00001254661,0.9927371,0.0006570112,0.0001305735,0.0001619132,0.000007368797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9170405,0.001659014,0.07662638,0.0005621045,0.000107126,0.0001464521,0.0008339597,0.001090669,0.001933775],"genre_scores_gemma":[0.9896855,0.000165307,0.008352757,0.00005953345,0.00002983227,0.00006628964,0.0007172293,0.0000176025,0.0009058193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01038381,"threshold_uncertainty_score":0.02064675,"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."}}