{"id":"W2276407298","doi":"10.1016/j.jmir.2015.12.075","title":"The Accuracy of Deformable Image Registration (DIR) in Modeling Organ Motion in Upper Gastro-Intestinal (GI) Cancers","year":2016,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Michener Institute; University of Toronto","funders":"","keywords":"Radiation therapy; Medicine; Nuclear medicine; Radiation treatment planning; Cone beam ct; Image registration; Pancreatic cancer; Residual; Cone beam computed tomography; Radiology; Computer science; Cancer; Computer vision; Computed tomography; Image (mathematics); Algorithm; Internal medicine","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":[0.002519725,0.0006015346,0.0004738107,0.001047095,0.0003093297,0.001142674,0.0006253261,0.0008526305,0.0006184001],"category_scores_gemma":[0.01049342,0.0004394875,0.0006814616,0.0006506208,0.0003594651,0.0008162822,0.0006364114,0.0007190576,0.0004181786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004425316,"about_ca_system_score_gemma":0.0005205639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007770751,"about_ca_topic_score_gemma":0.006543427,"domain_scores_codex":[0.999207,0.0003261396,0.00007347733,0.0001526447,0.0001895409,0.00005133381],"domain_scores_gemma":[0.9977275,0.001441595,0.0001915058,0.0003333486,0.0002587989,0.00004726099],"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":[0.001231881,0.000155455,0.03557133,0.0003711383,0.0003214584,0.0002134835,0.0003250494,0.529646,0.03843531,0.001732773,0.001564973,0.3904311],"study_design_scores_gemma":[0.000009132966,0.0001626829,0.007947897,0.0000202735,0.00008136957,0.0002360134,0.00005152619,0.975897,0.01406328,0.0004869612,0.001020464,0.00002344785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5946084,0.006212868,0.3917761,0.0005820083,0.0002481955,0.000140568,0.000604703,0.00213363,0.003693627],"genre_scores_gemma":[0.943661,0.001314736,0.05276053,0.00006486156,0.00003287848,0.0000209929,0.0005096223,0.0002911291,0.001344293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007770751,"threshold_uncertainty_score":0.01545101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100001697797704,"score_gpt":0.3212833759546042,"score_spread":0.3102833589766271,"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."}}