{"id":"W3010245316","doi":"10.1186/s12880-020-0418-1","title":"CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Juravinski Hospital; University of Toronto; Hamilton Health Sciences; Sunnybrook Health Science Centre; Sinai Health System; Health Sciences Centre; Lunenfeld-Tanenbaum Research Institute","funders":"Government of Ontario; Ontario Institute for Cancer Research","keywords":"Convolutional neural network; Radiomics; Pancreatic ductal adenocarcinoma; Proportional hazards model; Artificial intelligence; Computer science; Survival analysis; Hazard ratio; Deep learning; Medicine; Overall survival; Pattern recognition (psychology); Radiology; Oncology; Internal medicine; Pancreatic cancer; Cancer","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":[0.0004965712,0.0005403437,0.0003023331,0.0006156878,0.0001469652,0.0004246081,0.0007006844,0.0005153082,0.001572044],"category_scores_gemma":[0.00131689,0.0001974932,0.0006022666,0.0003692163,0.0002515782,0.000392766,0.0003718309,0.0005458978,0.000377188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198402,"about_ca_system_score_gemma":0.0007031622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01662941,"about_ca_topic_score_gemma":0.01333802,"domain_scores_codex":[0.9998844,0.00001808283,0.000007713375,0.00003920041,0.00002526715,0.00002535047],"domain_scores_gemma":[0.9997323,0.0001004713,0.00004590908,0.00001775442,0.00008771253,0.00001584702],"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":[0.0001718646,0.00005768901,0.017919,0.00008412253,0.00009114944,0.0002026621,0.0000510102,0.8941069,0.003105104,0.003193785,0.002128749,0.07888799],"study_design_scores_gemma":[0.000001656889,0.00001348672,0.001121981,0.000004082846,0.00001128165,0.00002811423,0.000002447859,0.9974922,0.0003691902,0.0007298932,0.0002229358,0.000002714214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3821316,0.002598325,0.6022021,0.001960387,0.0002336291,0.0001238725,0.003348216,0.001651329,0.005750616],"genre_scores_gemma":[0.9757447,0.0005116264,0.01862324,0.0001209039,0.00004923591,0.00008263964,0.001055934,0.00003100109,0.003780802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01662941,"threshold_uncertainty_score":0.0330652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06929486105858222,"score_gpt":0.373366540475042,"score_spread":0.3040716794164598,"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."}}