{"id":"W4212809016","doi":"10.21037/qims-21-980","title":"Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer","year":2022,"lang":"en","type":"article","venue":"Quantitative Imaging in Medicine and Surgery","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Nomogram; Radiomics; Medicine; Proliferation index; Receiver operating characteristic; Lung cancer; Cohort; Oncology; Stage (stratigraphy); Area under the curve; Internal medicine; Radiology; Immunohistochemistry; Biology","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":[0.001669345,0.0001111289,0.0003449039,0.0004503303,0.0001196683,0.000008483333,0.00003263481,0.00002273273,0.000005566344],"category_scores_gemma":[0.0004534174,0.00008765356,0.00002540873,0.000295692,0.0002276984,0.00006776219,0.00002295316,0.0002241698,8.054669e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006599362,"about_ca_system_score_gemma":0.0002120662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004721072,"about_ca_topic_score_gemma":0.00002740071,"domain_scores_codex":[0.9988554,0.00006867136,0.0005190166,0.0002102976,0.0002047665,0.0001418939],"domain_scores_gemma":[0.9982277,0.001329181,0.0002221531,0.00007396575,0.00009932215,0.00004768297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001950237,0.000134866,0.9674844,0.0006232949,0.00002946526,0.000005132375,0.008201875,0.003017556,0.008454669,0.000111103,0.0001786684,0.01156392],"study_design_scores_gemma":[0.003035979,0.00007998791,0.2643523,0.0005425042,0.00008490522,0.0000129823,0.004351765,0.7259781,0.001093458,0.00003083098,0.0003562418,0.00008090733],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340852,0.001861783,0.05870733,0.004535854,0.0001280667,0.0006460661,0.00001286167,0.000006797786,0.00001597713],"genre_scores_gemma":[0.9925325,0.0001749324,0.006564699,0.0003614774,0.00003081007,0.0002212086,0.00008896645,0.00001480444,0.00001059871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7229606,"threshold_uncertainty_score":0.3574409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03808206448449742,"score_gpt":0.3257728610687484,"score_spread":0.287690796584251,"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."}}