{"id":"W4402439209","doi":"10.1371/journal.pone.0307815","title":"Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep regional de Lanaudiere; Ericsson (Canada); Polytechnique Montréal; Hôpital du Sacré-Cœur de Montréal; Université Laval; Université de Montréal; Centre Intégré de Santé et de Services Sociaux des Laurentides; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données","keywords":"Medicine; Concordance; Colorectal cancer; Receiver operating characteristic; Confidence interval; Radiomics; Internal medicine; Oncology; Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005372994,0.0001654826,0.0009809857,0.001106451,0.00003315729,0.00002120784,0.0001120298,0.00009063529,0.0001485228],"category_scores_gemma":[0.000748115,0.000126393,0.000278248,0.002120628,0.00006169423,0.00004672811,0.00004800017,0.000262679,0.000001890754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001622839,"about_ca_system_score_gemma":0.00007012769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004535947,"about_ca_topic_score_gemma":0.0001067565,"domain_scores_codex":[0.9983733,0.00009366952,0.0004609299,0.000374021,0.0004093369,0.0002887459],"domain_scores_gemma":[0.9988102,0.0005805952,0.00006532642,0.0001570767,0.0001940146,0.0001928359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007807377,0.002863095,0.9770966,0.0003407146,0.007932465,0.00002745484,0.0002234473,0.0007222016,0.003260741,0.00002236582,0.0007305669,0.005999621],"study_design_scores_gemma":[0.001823657,0.001010178,0.5281146,0.0003598316,0.005826667,3.550901e-7,0.0000105171,0.4598905,0.002659792,0.000005787517,0.0001732224,0.0001249959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958258,0.0004590405,0.001132627,0.001095478,0.00006751078,0.0009375435,0.000334502,0.00008539102,0.0000620741],"genre_scores_gemma":[0.987357,0.00009300153,0.01148749,0.0003626558,0.00006327256,0.0001470529,0.0003483936,0.00002647366,0.0001147246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4591683,"threshold_uncertainty_score":0.5154158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181371587476539,"score_gpt":0.3025070113949286,"score_spread":0.2706932955201632,"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."}}