{"id":"W4409107652","doi":"10.1117/12.3043835","title":"Assessing the impact of a pre-processing pipeline on a tumor localization model in prostate MRI within a large multi-institutional dataset (NRG-GU005)","year":2025,"lang":"en","type":"article","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Pipeline (software); Computer science; Prostate; Magnetic resonance imaging; Artificial intelligence; Medicine; Radiology; Internal medicine; Cancer","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":[],"consensus_categories":[],"category_scores_codex":[0.005483556,0.001511028,0.001177955,0.001106597,0.0006164849,0.001407625,0.001411019,0.00133989,0.001214884],"category_scores_gemma":[0.00915978,0.0004324732,0.001637335,0.001099702,0.0005104337,0.0007989359,0.001109067,0.001167015,0.001384895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009123847,"about_ca_system_score_gemma":0.001787748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01374168,"about_ca_topic_score_gemma":0.01802943,"domain_scores_codex":[0.9977063,0.0008016278,0.0001696445,0.0008909256,0.000263979,0.0001674498],"domain_scores_gemma":[0.9972942,0.001455838,0.0002330675,0.0005321471,0.0003901869,0.00009451001],"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":[0.003118757,0.001257709,0.1164412,0.001149177,0.002179916,0.000699035,0.0004245528,0.3575331,0.04694305,0.0008923454,0.01960255,0.4497586],"study_design_scores_gemma":[0.0003106536,0.001654048,0.07152683,0.0001211632,0.0007187837,0.001229883,0.0003020427,0.8720985,0.04103224,0.001064613,0.009820866,0.0001203345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.809154,0.002253704,0.1618715,0.001172117,0.0003234659,0.0006380471,0.008697121,0.01400257,0.001887459],"genre_scores_gemma":[0.8176478,0.0003890372,0.153788,0.0003380783,0.00006954881,0.0003504638,0.02537734,0.0006279662,0.001411778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01374168,"threshold_uncertainty_score":0.02900016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200883141684854,"score_gpt":0.3903740536644769,"score_spread":0.3702857394959915,"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."}}