{"id":"W2132641021","doi":"10.1016/j.mri.2006.09.006","title":"Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":315,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Cancer Institute; U.S. Public Health Service; National Institute of Biomedical Imaging and Bioengineering; Vanderbilt University","keywords":"Magnetic resonance imaging; Voxel; Breast cancer; Nuclear medicine; Effective diffusion coefficient; Medicine; Dynamic contrast-enhanced MRI; Dynamic contrast; Diffusion MRI; Cancer; Nuclear magnetic resonance; Radiology; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003435239,0.0002073667,0.0003808876,0.0003728831,0.00006226858,0.00003038853,0.00007320124,0.00004189774,0.00004261298],"category_scores_gemma":[0.00008372871,0.0001937624,0.00004578277,0.0004413196,0.0001373564,0.0001085476,0.00003816198,0.0001169888,0.000003460163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000458498,"about_ca_system_score_gemma":0.00009552458,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01206322,"about_ca_topic_score_gemma":0.0009989896,"domain_scores_codex":[0.9983154,0.0001352437,0.0005626362,0.000438106,0.0002529093,0.0002956999],"domain_scores_gemma":[0.9990979,0.0002691917,0.0001304879,0.000276588,0.0001438076,0.00008195727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01283546,0.0005113076,0.2599553,0.0001299205,0.00001415454,0.0002884933,0.006863787,0.0002452641,0.1935667,0.000281613,0.004238384,0.5210696],"study_design_scores_gemma":[0.003892078,0.0008478374,0.971269,0.001570647,0.00003397067,0.00003641382,0.0008805417,0.001453018,0.01432003,0.00009275207,0.005415522,0.0001881871],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979705,0.006770682,0.000125566,0.01155188,0.0001034882,0.000726531,0.0001867919,0.00003216581,0.0007978596],"genre_scores_gemma":[0.9918002,0.000545255,0.0066211,0.0001602454,0.0001558889,0.0002547675,0.00001565564,0.0000274929,0.0004193649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7113137,"threshold_uncertainty_score":0.9945155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740622571095412,"score_gpt":0.3422559074086921,"score_spread":0.3148496816977379,"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."}}