{"id":"W2052507258","doi":"10.1371/journal.pone.0102107","title":"Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":603,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Princess Margaret Cancer Centre","funders":"Interreg; National Institutes of Health; National Cancer Institute; KWF Kankerbestrijding; National Center for Research Resources; Center for Translational Molecular Medicine; National Institute of Biomedical Imaging and Bioengineering; Health Foundation Limburg","keywords":"Radiomics; Contouring; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Robustness (evolution); Medical imaging; Reproducibility; Feature extraction; Feature (linguistics); Computer vision; Mathematics; Statistics","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":[0.0004660977,0.0001187938,0.0002877231,0.0002416482,0.0001109194,0.00004789278,0.00007841417,0.00009124251,0.00004542295],"category_scores_gemma":[0.0009428448,0.0001119648,0.00005062032,0.0004437028,0.00004638958,0.00009218352,0.00001835909,0.0003454419,0.00003924095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001346879,"about_ca_system_score_gemma":0.00004079243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002347264,"about_ca_topic_score_gemma":5.814726e-7,"domain_scores_codex":[0.9988562,0.00007421587,0.0002203393,0.000234295,0.0004175261,0.0001974563],"domain_scores_gemma":[0.9992562,0.0001027605,0.0001392856,0.0002709976,0.00009564604,0.0001351193],"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":[0.00005035181,0.001186831,0.05903313,0.0009995732,0.000368596,0.000008543232,0.0003552262,0.002052025,0.9037522,0.0002867026,0.001478113,0.03042872],"study_design_scores_gemma":[0.0009286535,0.00007803387,0.009885264,0.0003731802,0.0004658441,0.00002663197,0.00003819381,0.9751226,0.01276,0.00005590884,0.0001374313,0.0001282042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9025783,0.0001856955,0.09411187,0.002084063,0.00009202259,0.0003162678,0.000001264325,0.0001392391,0.000491299],"genre_scores_gemma":[0.7771683,0.000057429,0.2209896,0.0006534694,0.0003749999,0.00000954713,0.0000916548,0.00003928326,0.0006156295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9730706,"threshold_uncertainty_score":0.4565794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0569063703711607,"score_gpt":0.2794875699005049,"score_spread":0.2225811995293442,"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."}}