{"id":"W3044073468","doi":"10.1038/s41598-020-69106-8","title":"$$\\text {DRTOP}$$: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; Toronto General Hospital; Concordia University","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Mitacs; Government of Canada; Sunnybrook Research Institute","keywords":"Concordance correlation coefficient; Concordance; Artificial intelligence; Radiomics; Computer science; Hazard ratio; Algorithm; Machine learning; Lung cancer; Event (particle physics); Positron emission tomography; Proportional hazards model; Medicine; Nuclear medicine; Mathematics; Statistics; Internal medicine; Confidence interval; Physics","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":[0.0004650841,0.0008884926,0.0003865779,0.0006548314,0.000185117,0.0006167364,0.001334195,0.0008190047,0.01345829],"category_scores_gemma":[0.001511416,0.0002046173,0.0004592974,0.0005036312,0.0002283621,0.0005986968,0.0008154484,0.0006948999,0.006233034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005941865,"about_ca_system_score_gemma":0.000552242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007194537,"about_ca_topic_score_gemma":0.006385685,"domain_scores_codex":[0.9997374,0.00005706983,0.00001517065,0.00007947026,0.00007630352,0.00003458273],"domain_scores_gemma":[0.999743,0.0001000988,0.00002153453,0.00003723743,0.00007917122,0.00001885234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003997229,0.0002922616,0.003266163,0.0004525604,0.0001194534,0.0002195444,0.00003623532,0.158738,0.01044531,0.004472667,0.2878193,0.5337389],"study_design_scores_gemma":[0.00004833991,0.00007694888,0.001179296,0.00003849571,0.0000201098,0.00007721116,0.00000739712,0.9682031,0.007872124,0.003551485,0.01890485,0.00002058566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09929692,0.004827808,0.7092177,0.005502567,0.001557027,0.000579781,0.05114568,0.100215,0.02765752],"genre_scores_gemma":[0.6082212,0.002376322,0.2481456,0.002817137,0.0005539276,0.00109346,0.09301954,0.003007242,0.04076562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01345829,"threshold_uncertainty_score":0.04502243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351538231969917,"score_gpt":0.3053651834387694,"score_spread":0.2918498011190702,"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."}}