{"id":"W2894753707","doi":"10.1007/s00330-018-5754-y","title":"Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors","year":2018,"lang":"en","type":"article","venue":"European Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Tehran University of Medical Sciences and Health Services","keywords":"Kurtosis; Nuclear medicine; Coefficient of variation; Medicine; Iterative reconstruction; Imaging phantom; Similarity (geometry); Skewness; Mathematics; Biomedical engineering; Radiology; Statistics; Artificial intelligence; Computer science; Image (mathematics)","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":[0.0148121,0.0006471018,0.0006760043,0.001106214,0.0002969401,0.002593337,0.0005958439,0.001128782,0.0007461857],"category_scores_gemma":[0.07934715,0.0005123304,0.0008747386,0.0008956651,0.0006343839,0.001083648,0.0007590613,0.0008647401,0.0003676488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004785926,"about_ca_system_score_gemma":0.0005994379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001386384,"about_ca_topic_score_gemma":0.001399606,"domain_scores_codex":[0.9931964,0.004239601,0.0007512358,0.0006816458,0.0009854714,0.0001457384],"domain_scores_gemma":[0.9129872,0.07341802,0.00392493,0.004739225,0.004574447,0.0003561776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01107327,0.0003251529,0.2518528,0.001032453,0.003288281,0.0003904437,0.00107233,0.1610502,0.07740001,0.001717779,0.001486829,0.4893104],"study_design_scores_gemma":[0.0002286639,0.001008221,0.1808093,0.0002891299,0.001655277,0.002935073,0.000367609,0.6873716,0.1198919,0.002756514,0.002426041,0.0002606555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8534595,0.005060559,0.1380337,0.0004066543,0.0001656079,0.00005270551,0.0007246521,0.001013405,0.001083105],"genre_scores_gemma":[0.9661294,0.0007439364,0.0312901,0.0000783038,0.00004318548,0.0000220463,0.0006394143,0.0006502469,0.000403302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0148121,"threshold_uncertainty_score":0.07833481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644131493757322,"score_gpt":0.4002001680496842,"score_spread":0.373758853112111,"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."}}