{"id":"W4205334579","doi":"10.1016/j.compbiomed.2022.105230","title":"Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"BC Cancer Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Radiogenomics; KRAS; Feature (linguistics); Lung cancer; Cancer; Harmonization; Computer science; Computational biology; Medicine; Artificial intelligence; Radiomics; Oncology; Biology; Internal medicine; Colorectal cancer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002563491,0.0006054637,0.0005736655,0.0007198866,0.0002467214,0.0005026245,0.0003561487,0.0002491943,0.0003986852],"category_scores_gemma":[0.00420203,0.0001571702,0.0007976925,0.000491203,0.000335182,0.0004043793,0.0006147367,0.0003153082,0.0001625508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002581865,"about_ca_system_score_gemma":0.0003113431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001634149,"about_ca_topic_score_gemma":0.0009539992,"domain_scores_codex":[0.9989512,0.0004235619,0.00006895013,0.0002572232,0.0001825987,0.000116518],"domain_scores_gemma":[0.9988406,0.0004977387,0.0002509895,0.0001840333,0.000179475,0.00004717819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003765202,0.0008048746,0.2589548,0.0001629987,0.001123755,0.0002907603,0.0004474402,0.2691323,0.06045674,0.0003021597,0.001489465,0.4030696],"study_design_scores_gemma":[0.00007676516,0.002297332,0.2816893,0.00002870382,0.0005546457,0.000486231,0.0003246197,0.6716644,0.0407787,0.0004969615,0.001554368,0.00004797303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713534,0.0002217205,0.02753371,0.00007647243,0.00001509482,0.00004091248,0.0001169581,0.0001962345,0.0004455621],"genre_scores_gemma":[0.9945992,0.00003682218,0.004915635,0.0000133886,0.000008323483,0.00001368151,0.0002946034,0.00001367436,0.0001047418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002563491,"threshold_uncertainty_score":0.0135572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007789998847016184,"score_gpt":0.3044521610248692,"score_spread":0.296662162177853,"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."}}