{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002582831,0.000141646,0.0002693415,0.0001386722,0.00009614592,0.00003190302,0.00002430498,0.00004970733,0.000001088662],"category_scores_gemma":[0.0004108519,0.0001362472,0.00002985847,0.00009195939,0.0001362019,0.00009713383,0.00004374163,0.0002275845,5.433471e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006209494,"about_ca_system_score_gemma":0.00004381284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003678259,"about_ca_topic_score_gemma":0.000001962166,"domain_scores_codex":[0.9989643,0.00009767643,0.0002977722,0.0003443367,0.00006451844,0.0002314421],"domain_scores_gemma":[0.9995759,0.00005324656,0.000123642,0.00005360822,0.00006728464,0.0001262776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005460778,0.00001399776,0.3813446,0.00008610129,0.00002124991,0.00001193967,0.0007159275,0.0006057943,0.5069951,0.00002323327,0.000002550538,0.1101249],"study_design_scores_gemma":[0.002921299,0.0001617515,0.01357546,0.00006155067,0.00007447817,0.00006978089,0.0002224473,0.9787784,0.003803351,0.00005152206,0.0001718997,0.0001081029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8975608,0.002581304,0.09852718,0.0009194153,0.00007288683,0.0002821962,0.00002376848,0.00002334063,0.000009159827],"genre_scores_gemma":[0.9758544,0.0002140226,0.0228708,0.0006214458,0.00008381552,0.000005832087,0.0003253785,0.00002340889,9.152618e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9781725,"threshold_uncertainty_score":0.5556001,"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."}}