{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006606671,0.001391751,0.001004039,0.004449974,0.0004901958,0.001640975,0.0008916333,0.001000562,0.0006193869],"category_scores_gemma":[0.01171668,0.0003207652,0.001167798,0.001083247,0.0004336639,0.0009126968,0.0007989571,0.0008832318,0.0004113247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008359713,"about_ca_system_score_gemma":0.001018588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001658011,"about_ca_topic_score_gemma":0.00209432,"domain_scores_codex":[0.9984345,0.0005831516,0.0001709887,0.0002672099,0.0004262053,0.000117903],"domain_scores_gemma":[0.9948054,0.002543735,0.0006962546,0.0002941038,0.001388705,0.0002718488],"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":[0.00174037,0.0006862803,0.4113192,0.0003696887,0.0008981979,0.0004746752,0.0002918449,0.1539466,0.01393628,0.001115712,0.007469103,0.407752],"study_design_scores_gemma":[0.0001414634,0.0008200674,0.1302259,0.00008573411,0.0003593837,0.0006501084,0.0001248744,0.8547712,0.008584685,0.001136182,0.003001729,0.00009870627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7274316,0.002243397,0.2620578,0.0005775776,0.0001475309,0.0005183601,0.002922331,0.002228399,0.001873092],"genre_scores_gemma":[0.929061,0.000257135,0.06804274,0.00006584774,0.00005608324,0.0003418135,0.001810324,0.00005001808,0.0003151394],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006606671,"threshold_uncertainty_score":0.03493983,"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."}}