{"id":"W3133146641","doi":"10.1111/his.14353","title":"Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters","year":2021,"lang":"en","type":"article","venue":"Histopathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of British Columbia","funders":"","keywords":"Tumor budding; Perineural invasion; Colorectal cancer; Medicine; Algorithm; Carcinoma; Pathology; Lymph node; Metastasis; Lymphovascular invasion; Oncology; Cancer; Internal medicine; Lymph node metastasis; Computer science","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":[0.004173343,0.0007887115,0.0003783116,0.0008588874,0.0002661856,0.0008330112,0.0009902408,0.001085033,0.0009457191],"category_scores_gemma":[0.004893298,0.0003202635,0.0004558405,0.0003476118,0.0004473553,0.0005959186,0.000819271,0.000868936,0.0004397476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009500436,"about_ca_system_score_gemma":0.001037619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003868531,"about_ca_topic_score_gemma":0.003547211,"domain_scores_codex":[0.9991331,0.0002365794,0.00008228178,0.000194106,0.000263312,0.0000905851],"domain_scores_gemma":[0.9973992,0.0008655588,0.0001475132,0.0002459485,0.001242601,0.00009916647],"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":[0.0004302784,0.0005241813,0.05612336,0.0001795304,0.0002041133,0.0001565227,0.0001713148,0.253114,0.1851792,0.001981621,0.002199905,0.499736],"study_design_scores_gemma":[0.00003010695,0.0002175933,0.009355192,0.00002587813,0.00002315226,0.00009285346,0.00002539925,0.944846,0.04351854,0.0006352518,0.001210758,0.00001928904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4300246,0.0004188606,0.5643499,0.0003074934,0.00006859545,0.0004087866,0.0005079178,0.001812148,0.002101637],"genre_scores_gemma":[0.6032491,0.0001101576,0.3933839,0.0001664093,0.00001542087,0.0003131159,0.0008334285,0.00009010793,0.001838357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004173343,"threshold_uncertainty_score":0.022071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852158249164454,"score_gpt":0.2786131738773993,"score_spread":0.2500915913857548,"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."}}