{"id":"W2954503654","doi":"10.1007/s11307-020-01487-8","title":"Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms","year":2020,"lang":"en","type":"article","venue":"Molecular Imaging and Biology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":163,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia Hospital; University of British Columbia","funders":"Shaheed Rajaei Cardiovascular Medical and Research Center; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Radiogenomics; KRAS; Machine learning; Computer science; Artificial intelligence; Mutation; Algorithm; Radiomics; Biology; Genetics; Gene","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":[0.0006286788,0.0004191452,0.0005631247,0.001123637,0.0001941215,0.0006495979,0.0003489494,0.0006609594,0.0008822788],"category_scores_gemma":[0.001343281,0.0002091862,0.0004777874,0.0003451459,0.0001547505,0.0004080432,0.0003510928,0.0005124059,0.0002685456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003071908,"about_ca_system_score_gemma":0.0003429269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099141,"about_ca_topic_score_gemma":0.002807027,"domain_scores_codex":[0.9997502,0.00007381987,0.00002267446,0.00007755469,0.00004456986,0.00003116803],"domain_scores_gemma":[0.9996587,0.0001778505,0.00004919129,0.00001917612,0.00006512195,0.0000299935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002296606,0.0005435774,0.5304814,0.0003379158,0.0007730122,0.001168833,0.000200477,0.01938967,0.1249854,0.001591917,0.004132086,0.3140991],"study_design_scores_gemma":[0.0002422361,0.0009927904,0.2790004,0.0002139518,0.001239604,0.003750931,0.0003667002,0.6167061,0.07863049,0.007671895,0.01107257,0.0001122223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607477,0.005778432,0.02847896,0.0009214283,0.00009253333,0.00009645841,0.001479414,0.0002690831,0.002135965],"genre_scores_gemma":[0.9753903,0.0009228315,0.02119999,0.0003572238,0.00006608586,0.00005786385,0.001224481,0.00002846769,0.0007528383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001123637,"threshold_uncertainty_score":0.003324807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341848152830091,"score_gpt":0.2836021825735917,"score_spread":0.2494173672905826,"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."}}