{"id":"W4403869490","doi":"10.1002/mp.17490","title":"Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization","year":2024,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Reproducibility; Medical physics; Nuclear medicine; Single-photon emission computed tomography; Myocardial perfusion imaging; Harmonization; Medicine; Medical imaging; Radiology; Computer science; Artificial intelligence; Perfusion; Mathematics; Statistics; Physics","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":[0.007702445,0.0006147824,0.0005892071,0.001037,0.0003503975,0.0009112175,0.0004732758,0.0004969171,0.0006891855],"category_scores_gemma":[0.02278025,0.000262996,0.0007881273,0.0007881848,0.0008349976,0.0005327787,0.0008527925,0.0003441065,0.0002016584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287166,"about_ca_system_score_gemma":0.0003372486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005723605,"about_ca_topic_score_gemma":0.0004460164,"domain_scores_codex":[0.9958525,0.001909505,0.0004993804,0.0007655636,0.0007948631,0.0001781316],"domain_scores_gemma":[0.9861043,0.007184118,0.002032527,0.00230264,0.002182269,0.00019419],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01562219,0.0006579513,0.3193659,0.001009235,0.0019552,0.0006104369,0.001892812,0.06199155,0.1712077,0.0008689485,0.001003121,0.423815],"study_design_scores_gemma":[0.0002123524,0.006256786,0.6245002,0.0001338313,0.001662189,0.003006058,0.0006230933,0.1544911,0.2046856,0.001066533,0.003153676,0.0002087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504613,0.0006719271,0.04720601,0.00007021487,0.00003776414,0.0001186094,0.0001875402,0.0003660423,0.000880709],"genre_scores_gemma":[0.9805189,0.0001054408,0.01874341,0.00002309669,0.00002244176,0.00004865622,0.0002746279,0.000118567,0.0001448541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9922975,"threshold_uncertainty_score":0.04073489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009065593274364462,"score_gpt":0.3271504546212921,"score_spread":0.3180848613469276,"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."}}