{"id":"W3154535333","doi":"10.2214/ajr.21.25456","title":"Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability","year":2021,"lang":"en","type":"article","venue":"American Journal of Roentgenology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Kurtosis; Medicine; Intraclass correlation; Software; Pearson product-moment correlation coefficient; Texture (cosmology); Reproducibility; Artificial intelligence; Pattern recognition (psychology); Skewness; Segmentation; Correlation; Image texture; Renal cell carcinoma; Nuclear medicine; Statistics; Image segmentation; Computer science; Pathology; Mathematics; 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":[0.01792553,0.0007867189,0.001348892,0.002551786,0.0006154349,0.002507268,0.0006794847,0.000723761,0.001281298],"category_scores_gemma":[0.05188395,0.0004738629,0.001313312,0.001867059,0.0006080064,0.001427798,0.002470666,0.000906729,0.0005468691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004738652,"about_ca_system_score_gemma":0.0007857349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001578915,"about_ca_topic_score_gemma":0.002216949,"domain_scores_codex":[0.9886907,0.004573518,0.001161294,0.001136905,0.00400651,0.0004311374],"domain_scores_gemma":[0.9582816,0.01728329,0.004606078,0.005428532,0.01349487,0.0009057291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003401399,0.0002202338,0.8003079,0.0002590011,0.0009612668,0.0004514541,0.001318291,0.006695404,0.01033585,0.0003530244,0.001304465,0.1743917],"study_design_scores_gemma":[0.0001268552,0.003526842,0.8923404,0.0001245849,0.0008092051,0.002817317,0.002175759,0.0823176,0.01015386,0.002441596,0.002982025,0.0001839287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500416,0.0007955494,0.04517776,0.0003190425,0.00008590344,0.0002543252,0.0005968764,0.0003790852,0.002349838],"genre_scores_gemma":[0.9800001,0.0001349256,0.01919851,0.00003831679,0.00002419617,0.00006462818,0.0002148345,0.0001038141,0.0002207112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01792553,"threshold_uncertainty_score":0.09480041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00918467161824805,"score_gpt":0.3579846007332723,"score_spread":0.3487999291150243,"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."}}