{"id":"W2894069389","doi":"10.1002/jmri.26327","title":"Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis","year":2018,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Bladder and Urothelial Cancer Treatments","field":"Medicine","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Cancer Institute; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Bladder cancer; Radiomics; Receiver operating characteristic; Discriminative model; Medicine; Diffusion MRI; Support vector machine; Effective diffusion coefficient; Mann–Whitney U test; Artificial intelligence; Radiology; Computer science; Magnetic resonance imaging; Cancer; Internal medicine","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":[0.001041313,0.0002813908,0.0003237728,0.001603491,0.0001052847,0.0003509106,0.0001614677,0.0002702539,0.0005174315],"category_scores_gemma":[0.00238809,0.0001346072,0.0002834904,0.0003741496,0.0001996537,0.0002747518,0.0002818704,0.0001198859,0.0001367884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232619,"about_ca_system_score_gemma":0.0001234419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003525663,"about_ca_topic_score_gemma":0.0004310275,"domain_scores_codex":[0.9997464,0.00008277073,0.00003877034,0.00004834208,0.00005803522,0.00002573974],"domain_scores_gemma":[0.9988999,0.0004941209,0.0002567899,0.0001010777,0.0001684829,0.00007960719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001621613,0.0001490292,0.8174701,0.000225019,0.0002149585,0.0003529256,0.0001701899,0.003377036,0.09096462,0.0001295726,0.0002262372,0.08509875],"study_design_scores_gemma":[0.00002710557,0.0007060413,0.9522101,0.00002040177,0.0002128378,0.002677356,0.0001779583,0.02252288,0.02051507,0.0002180125,0.0006797648,0.00003250702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915169,0.0005209231,0.007520547,0.00001748231,0.000003746801,0.00002475459,0.0001580541,0.00002853771,0.0002089669],"genre_scores_gemma":[0.9964898,0.00007849168,0.003201296,0.000006149815,0.000007430066,0.00001795954,0.0001384984,0.000003437638,0.0000567891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001603491,"threshold_uncertainty_score":0.005507052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800361044754601,"score_gpt":0.2911347290153263,"score_spread":0.2731311185677803,"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."}}