{"id":"W2953522270","doi":"10.1002/acm2.12654","title":"Tissue segmentation‐based electron density mapping for MR‐only radiotherapy treatment planning of brain using conventional T1‐weighted MR images","year":2019,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Laurentian University; Lakehead University; Sunnybrook Health Science Centre; Northeast Cancer Centre; University of Toronto; Health Sciences North","funders":"","keywords":"Voxel; Nuclear medicine; Magnetic resonance imaging; Radiation treatment planning; Soft tissue; Skull; Radiation therapy; Radiosurgery; Medicine; Segmentation; Radiology; Computer science; Anatomy; Artificial intelligence","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":[0.0006512856,0.0004808426,0.0002269776,0.0007444808,0.0001847402,0.0004670112,0.0004692685,0.0003274903,0.0009700019],"category_scores_gemma":[0.001855983,0.0003887535,0.0004283773,0.0003645138,0.0002708856,0.0003647368,0.0003093908,0.0002350211,0.0003473243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477935,"about_ca_system_score_gemma":0.0006841942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001345273,"about_ca_topic_score_gemma":0.001870132,"domain_scores_codex":[0.9997858,0.00005619232,0.00001978523,0.00003868629,0.00009008001,0.000009378668],"domain_scores_gemma":[0.9995523,0.0001724591,0.00006998986,0.00006385217,0.000126691,0.00001480055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005035638,0.0001066434,0.008198211,0.0004929206,0.0001187625,0.0004288629,0.000339498,0.1655137,0.4819554,0.002048742,0.001201077,0.3390927],"study_design_scores_gemma":[0.00006100926,0.0003670102,0.01564499,0.00004569688,0.000112873,0.002010445,0.0001056033,0.7083466,0.2642842,0.002229814,0.006714799,0.00007691338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07739154,0.0003301745,0.9201203,0.00004857894,0.00001645844,0.0001429411,0.00009063778,0.0009798583,0.0008796262],"genre_scores_gemma":[0.3132664,0.0002453483,0.6849719,0.00003409339,0.000009968689,0.0001754943,0.0002342992,0.000313268,0.000749326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001345273,"threshold_uncertainty_score":0.003444374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129413640980757,"score_gpt":0.3932205124785831,"score_spread":0.3619263760687755,"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."}}