{"id":"W4393930088","doi":"10.1007/s12149-024-01923-7","title":"The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods","year":2024,"lang":"en","type":"article","venue":"Annals of Nuclear Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"Université de Genève; Tehran University of Medical Sciences and Health Services; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Medicine; Harmonization; Segmentation; Medical physics; Nuclear medicine; Artificial intelligence; Radiology","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":[0.005183721,0.0001352779,0.0004286678,0.0001219386,0.0001042506,0.000008356048,0.0001428558,0.00005257924,0.0001220608],"category_scores_gemma":[0.00436196,0.0000643452,0.0001161182,0.0003802813,0.0005274946,0.00004224209,0.00002820626,0.0004169654,0.000001085758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002319145,"about_ca_system_score_gemma":0.00005144135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001461519,"about_ca_topic_score_gemma":2.753105e-7,"domain_scores_codex":[0.9980302,0.0007404902,0.0004765556,0.000184052,0.0004203423,0.0001483305],"domain_scores_gemma":[0.9960682,0.003149666,0.0002363676,0.0003338393,0.0001478029,0.00006411525],"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":[0.00124637,0.0001220584,0.002463533,0.00149883,0.0007514455,0.00004683124,0.003120021,0.0004169262,0.8424914,0.005466899,0.01486069,0.127515],"study_design_scores_gemma":[0.00593372,0.01161827,0.1513201,0.009224795,0.003182719,0.0009961192,0.001364539,0.5547571,0.2346402,0.004095391,0.0223202,0.0005468449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690295,0.001553742,0.004625422,0.02282971,0.0004573961,0.0005110333,0.000004276737,0.00003987144,0.0009490703],"genre_scores_gemma":[0.9960831,0.0003933672,0.002642468,0.0006147299,0.0001628705,0.000002700394,0.00001119598,0.00003108755,0.00005843576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6078513,"threshold_uncertainty_score":0.5221989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03503470101941309,"score_gpt":0.4098537627301411,"score_spread":0.374819061710728,"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."}}