{"id":"W4302283548","doi":"10.1101/2022.10.03.22280659","title":"Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Institute of Cancer Research; Ontario Institute for Cancer Research; Public Health Ontario; University of British Columbia; Lunenfeld-Tanenbaum Research Institute; Brock University; Princess Margaret Cancer Centre; BC Cancer Agency; University of Toronto","funders":"","keywords":"Lasso (programming language); Radiomics; Cross-validation; Hyperparameter; Calibration; Test set; Malignancy; Random forest; Artificial intelligence; Machine learning; Hyperparameter optimization; Medicine; Lung cancer screening; Lung cancer; Computer science; Support vector machine; Statistics; Mathematics; Pathology","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":[0.007044434,0.000964074,0.0007980068,0.002953176,0.0002594099,0.00126165,0.0008502153,0.000789656,0.0009986089],"category_scores_gemma":[0.01374146,0.000283415,0.001451672,0.0009717423,0.0004265145,0.0006222489,0.0006748723,0.000721217,0.0003810811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006368892,"about_ca_system_score_gemma":0.0006340004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00310589,"about_ca_topic_score_gemma":0.002026191,"domain_scores_codex":[0.9979473,0.001171117,0.0001470247,0.0003650238,0.0002708123,0.00009880481],"domain_scores_gemma":[0.9929455,0.004760098,0.001066051,0.0003167951,0.0007684182,0.0001431635],"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":[0.0006782284,0.0003702892,0.4031534,0.0002186084,0.0009676743,0.0002751149,0.0001083053,0.5008023,0.002931471,0.001343303,0.002319518,0.08683183],"study_design_scores_gemma":[0.00001706438,0.0001354408,0.02350183,0.00003410111,0.0000674812,0.0001013381,0.00002663112,0.9732106,0.001001497,0.001504155,0.0003812759,0.00001855499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7108916,0.002799232,0.2805176,0.0009548946,0.00008822636,0.0001660907,0.001229883,0.001143207,0.002209339],"genre_scores_gemma":[0.9802225,0.0001305365,0.01842616,0.0001162444,0.000035827,0.00006050285,0.0006863974,0.00002459384,0.0002972119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007044434,"threshold_uncertainty_score":0.03725499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0568803108949348,"score_gpt":0.2946220479497039,"score_spread":0.2377417370547691,"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."}}