{"id":"W2023560768","doi":"10.1016/j.ijrobp.2007.04.004","title":"Development of Multiorgan Finite Element-Based Prostate Deformation Model Enabling Registration of Endorectal Coil Magnetic Resonance Imaging for Radiotherapy Planning","year":2007,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; Mount Sinai Hospital; Princess Margaret Cancer Centre","funders":"","keywords":"Fiducial marker; Medicine; Magnetic resonance imaging; Prostate; Prostate cancer; Radiation treatment planning; Deformation (meteorology); Nuclear medicine; Image-guided radiation therapy; Radiation therapy; Radiology; Medical imaging; Image registration; Artificial intelligence; Cancer; Materials science; Computer science","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":[0.0004198494,0.0006180356,0.0006452479,0.0004516013,0.0003032239,0.0006507666,0.001539354,0.001183563,0.002305523],"category_scores_gemma":[0.0009109235,0.0008780952,0.0009151633,0.0004314312,0.0002344975,0.0006106409,0.0006160015,0.0008758707,0.001116628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004204668,"about_ca_system_score_gemma":0.001186777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004867154,"about_ca_topic_score_gemma":0.007170415,"domain_scores_codex":[0.9997478,0.00003819222,0.0000189856,0.00004582629,0.000133802,0.00001541711],"domain_scores_gemma":[0.9997024,0.0000771608,0.00003920045,0.0000601125,0.0001015763,0.00001959958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004598286,0.00006814087,0.0007947554,0.00009792038,0.00005524429,0.0001294251,0.000106924,0.893586,0.03840737,0.004171858,0.001363725,0.06117276],"study_design_scores_gemma":[0.000005029961,0.00001644463,0.0002290804,0.000007876933,0.000009280526,0.00005684176,0.000008616997,0.9898644,0.007041408,0.0005160123,0.002234054,0.00001092949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005427425,0.00005061162,0.9924579,0.00005380801,0.00001841059,0.00004906354,0.0001195579,0.001042314,0.000780988],"genre_scores_gemma":[0.2932002,0.0003148473,0.699391,0.0001047781,0.00001305445,0.0003112529,0.0008261431,0.0007058823,0.005132894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004867154,"threshold_uncertainty_score":0.009677649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654930782733799,"score_gpt":0.3286042025857152,"score_spread":0.3120548947583772,"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."}}