{"id":"W3010936339","doi":"10.3390/s20051539","title":"Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Radiomics; Prostate cancer; Medicine; Diffusion MRI; Effective diffusion coefficient; Prostatectomy; Medical physics; Computer science; Artificial intelligence; Cancer; Magnetic resonance imaging; Radiology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006414742,0.0007298354,0.0004278484,0.0004355065,0.0001445139,0.0006437865,0.0008584163,0.0007232729,0.000790059],"category_scores_gemma":[0.001643208,0.0002740227,0.0004211979,0.0002115668,0.0004507581,0.0008205496,0.000732547,0.0005681907,0.0004384874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002853217,"about_ca_system_score_gemma":0.0002994453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003177017,"about_ca_topic_score_gemma":0.0005433511,"domain_scores_codex":[0.9995831,0.00007489043,0.00002898694,0.0001333831,0.0001436858,0.00003596889],"domain_scores_gemma":[0.9994057,0.0002105396,0.0001524973,0.00006642648,0.0001272424,0.00003753358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003775012,0.000105224,0.001669195,0.0003931168,0.00005606952,0.0003491832,0.0001046124,0.02663975,0.8453426,0.002805155,0.0007790817,0.1213786],"study_design_scores_gemma":[0.00005398492,0.001148883,0.002116425,0.00003415766,0.0001081544,0.001286802,0.00008273627,0.3162315,0.6667063,0.003040698,0.009095112,0.00009531536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1379194,0.003177322,0.8527417,0.0003643381,0.0001379209,0.0002387448,0.0001329494,0.001303317,0.003984372],"genre_scores_gemma":[0.7572737,0.001163738,0.237502,0.0007191495,0.00007331806,0.0001789468,0.0001993469,0.0001055779,0.002784303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008584163,"threshold_uncertainty_score":0.003392518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03288113366424029,"score_gpt":0.2873414583752006,"score_spread":0.2544603247109604,"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."}}