{"id":"W4393227005","doi":"10.1007/s11517-024-03071-6","title":"Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study","year":2024,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University; Douglas Mental Health University Institute","funders":"Université de Genève; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Reproducibility; Imaging phantom; Biomedical engineering; Image processing; Human physiology; Medical physics; Artificial intelligence; Computer vision; Computer science; Nuclear medicine; Pattern recognition (psychology); Medicine; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007229286,0.0007524631,0.0006915745,0.00081858,0.0003783963,0.0008325637,0.0006705266,0.0005949124,0.0009184915],"category_scores_gemma":[0.02681326,0.0004028657,0.0005317144,0.0008463943,0.0006905125,0.0006595078,0.001114306,0.0002959339,0.0004108987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710515,"about_ca_system_score_gemma":0.0002280814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00030554,"about_ca_topic_score_gemma":0.0002868196,"domain_scores_codex":[0.9932105,0.003187426,0.0005863958,0.001364497,0.001434242,0.0002169052],"domain_scores_gemma":[0.9794452,0.01102127,0.002737808,0.004630769,0.001979904,0.0001851643],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006887977,0.0003740418,0.02489336,0.0007365851,0.0008726451,0.0005984158,0.001979952,0.01953571,0.7859921,0.0004859892,0.0006242794,0.157019],"study_design_scores_gemma":[0.0002073424,0.01248553,0.205272,0.0001101263,0.001541771,0.004818199,0.0008126133,0.04807608,0.7143,0.001239027,0.01084966,0.0002876376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8749878,0.001974632,0.1203543,0.0001427352,0.0001066039,0.0002133428,0.0002276929,0.0007950001,0.001197814],"genre_scores_gemma":[0.9512269,0.0003720114,0.04650216,0.0001635941,0.00005971901,0.0001611048,0.0004044721,0.0004913576,0.0006185313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9927707,"threshold_uncertainty_score":0.03823256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860416009624154,"score_gpt":0.3242742442032294,"score_spread":0.3056700841069879,"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."}}