{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004767072,0.0002127027,0.0006848326,0.0009969826,0.00008661556,0.0000449768,0.0001535878,0.00005604134,0.0000850391],"category_scores_gemma":[0.0007303809,0.000179244,0.0002805289,0.001354604,0.0004226638,0.00025514,0.00003988071,0.0001972428,0.000005874544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191669,"about_ca_system_score_gemma":0.000315461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001550733,"about_ca_topic_score_gemma":0.00004837834,"domain_scores_codex":[0.9978867,0.0001043981,0.0009330741,0.00030752,0.0005059177,0.0002624276],"domain_scores_gemma":[0.9971677,0.0003986717,0.0008447734,0.0003191697,0.001098218,0.0001714835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00164989,0.0006323831,0.7083541,0.0002480242,0.001378886,0.0003734908,0.008620255,0.00005865838,0.1055908,0.00009946914,0.001080494,0.1719136],"study_design_scores_gemma":[0.00354596,0.001552808,0.9675455,0.0002307306,0.003049157,0.0002256655,0.001711473,0.004112998,0.01721065,0.0001728857,0.0004598567,0.0001822913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960632,0.03522579,0.002662375,0.0005788284,0.0002071621,0.0003342938,0.00003406448,0.000008798283,0.0003166958],"genre_scores_gemma":[0.9869577,0.002986145,0.009498755,0.0001734092,0.000194241,0.000007154169,0.000003733393,0.0000252418,0.0001535717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2591914,"threshold_uncertainty_score":0.730936,"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."}}