{"id":"W2085096920","doi":"10.1118/1.3685578","title":"Evaluation of a lung tumor autocontouring algorithm for intrafractional tumor tracking using low-field MRI: A phantom study","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Health Solutions; Alberta Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; Accuray","keywords":"Imaging phantom; Lung tumor; Nuclear medicine; Tracking (education); Centroid; Computer science; Algorithm; Scanner; Medical imaging; Voxel; Lung cancer; Artificial intelligence; Medicine; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002776388,0.0001789146,0.0004402433,0.00006969317,0.00008654796,0.00001309554,0.0001167544,0.00006950515,0.0004747498],"category_scores_gemma":[0.0009485314,0.0001656158,0.0001480361,0.0002230554,0.00005593482,0.0002698133,0.00004687862,0.0003158685,0.000003471451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004179756,"about_ca_system_score_gemma":0.000712358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001107752,"about_ca_topic_score_gemma":0.000006171959,"domain_scores_codex":[0.9964067,0.0001178955,0.0004722443,0.000260313,0.002345232,0.0003976619],"domain_scores_gemma":[0.9980782,0.0006096976,0.0002386411,0.0002378222,0.0005443065,0.0002913375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002000247,0.00576217,0.04122145,0.0005080291,0.0006670508,0.00002325275,0.002457925,0.0001157661,0.002484949,0.00003491119,0.002614023,0.9439104],"study_design_scores_gemma":[0.01071056,0.0005813056,0.01453042,0.001227153,0.003092978,0.00007436056,0.0007250824,0.8636149,0.1041165,0.0005244013,0.000399743,0.0004025783],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8354313,0.0004009857,0.1595787,0.0005031588,0.001028826,0.002812985,0.00001845848,0.00006185497,0.0001636563],"genre_scores_gemma":[0.9914063,0.000005873107,0.003728874,0.0005749767,0.003788731,0.0004406589,0.00001190089,0.00003877327,0.000003925335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9435079,"threshold_uncertainty_score":0.6753618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07514068075462312,"score_gpt":0.4073177703168664,"score_spread":0.3321770895622433,"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."}}