{"id":"W3164666727","doi":"10.1088/2057-1976/ac0501","title":"Feasibility of using a single MRI acquisition for fiducial marker localization and synthetic CT generation towards MRI-only prostate radiation therapy treatment planning","year":2021,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Fiducial marker; Prostate cancer; Medicine; Prostate; Nuclear medicine; Magnetic resonance imaging; Soft tissue; Segmentation; Radiation treatment planning; Radiology; Radiation therapy; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008544542,0.0001812574,0.0002165172,0.00004974582,0.00006266494,0.00003608203,0.00004014064,0.00005058934,0.000004060428],"category_scores_gemma":[0.00002018608,0.0001865088,0.00005927616,0.0001655525,0.00003497056,0.0002156231,0.00001426521,0.00005484618,1.808551e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655631,"about_ca_system_score_gemma":0.00004166545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004529569,"about_ca_topic_score_gemma":7.734157e-8,"domain_scores_codex":[0.9991208,0.00002103808,0.0002487102,0.0002371943,0.0001652085,0.0002070613],"domain_scores_gemma":[0.9996178,0.00004768858,0.00004933507,0.0001508091,0.00006409659,0.00007030171],"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":[0.00002138445,0.0000952356,0.0002326284,0.00014578,0.00004030118,0.000003263596,0.0004284582,0.3932322,0.57859,0.00005526797,0.00001740831,0.0271381],"study_design_scores_gemma":[0.000879587,0.00006581017,0.0004591873,0.0001239514,0.00003226074,0.000008771433,0.00003337947,0.8918138,0.103628,0.0001818872,0.00256638,0.0002070342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3745273,0.0007080081,0.6242127,0.00001270684,0.0002140365,0.0002070318,0.00003820954,0.00007736772,0.00000260185],"genre_scores_gemma":[0.9650947,0.0001254951,0.03409292,0.00001084763,0.0003417302,0.00004172554,0.0002453273,0.00004346064,0.000003756339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5905674,"threshold_uncertainty_score":0.7605608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02708043211275339,"score_gpt":0.2706983050468487,"score_spread":0.2436178729340953,"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."}}