{"id":"W4391212953","doi":"10.1016/j.labinv.2024.100341","title":"Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer","year":2024,"lang":"en","type":"article","venue":"Laboratory Investigation","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Ontario Institute for Cancer Research; University Health Network; Toronto Metropolitan University; University of Toronto; Princess Margaret Cancer Centre","funders":"Canadian Institutes of Health Research; Canadian Cancer Society; Ontario Ministry of Health and Long-Term Care","keywords":"Digital image analysis; Breast cancer; Reliability (semiconductor); Digital image; Medicine; Cancer; Pathology; Internal medicine; Computer science; Image processing; Oncology; Image (mathematics); Artificial intelligence; Computer vision; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03835903,0.0008776982,0.0008122045,0.002802247,0.0007786954,0.002195604,0.001119143,0.001113316,0.0006929206],"category_scores_gemma":[0.1012195,0.0004787506,0.0009540847,0.001439795,0.001324095,0.0008614545,0.001917244,0.001013895,0.0006468488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008025144,"about_ca_system_score_gemma":0.0007007883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002240765,"about_ca_topic_score_gemma":0.002601973,"domain_scores_codex":[0.9618617,0.01446061,0.00398412,0.009195451,0.009728062,0.0007701271],"domain_scores_gemma":[0.9213735,0.04713473,0.006685296,0.00949736,0.01457605,0.0007330939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003933231,0.0002908733,0.5974165,0.001476477,0.003541003,0.0002800143,0.003579566,0.02008646,0.04586985,0.002243654,0.007013694,0.3142686],"study_design_scores_gemma":[0.0001961474,0.001158918,0.7666572,0.00068853,0.001284265,0.001867781,0.001092097,0.1413326,0.06146604,0.00775709,0.01613974,0.0003596168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8200502,0.01164802,0.1549925,0.0005806779,0.0009493573,0.0004719357,0.001514873,0.001832079,0.007960373],"genre_scores_gemma":[0.971916,0.0005010761,0.02490197,0.000215644,0.0001030884,0.0002314685,0.001173898,0.0003122041,0.0006446461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03835903,"threshold_uncertainty_score":0.2028643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02025778249634775,"score_gpt":0.416403441123627,"score_spread":0.3961456586272793,"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."}}