{"id":"W4410870007","doi":"10.18280/rces.120101","title":"Predicting Lymph Node Metastasis in T1 Colorectal Cancer Patients Using Interpretable Machine Learning Models: A Multicenter Retrospective Study","year":2025,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lymph node metastasis; Colorectal cancer; Medicine; Lymph node; Multicenter study; Retrospective cohort study; Metastasis; Cancer; Oncology; Artificial intelligence; General surgery; Internal medicine; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001968876,0.0004021347,0.000382087,0.0009332324,0.0003333576,0.0005732403,0.0003509237,0.0003755238,0.000554722],"category_scores_gemma":[0.004806738,0.0002593983,0.0005591654,0.001108484,0.000232464,0.0003958244,0.0003171267,0.0004046358,0.0001816805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004029726,"about_ca_system_score_gemma":0.00041738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004634331,"about_ca_topic_score_gemma":0.004360473,"domain_scores_codex":[0.9993679,0.0002380073,0.00007982676,0.000164069,0.0000981809,0.00005204909],"domain_scores_gemma":[0.9974803,0.0009711285,0.0006158366,0.0003552532,0.0003984574,0.0001790186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002248829,0.00006325214,0.9957311,0.00001613375,0.0000871916,0.0000897858,0.00006433012,0.0003195569,0.000183441,0.000014788,0.0001092599,0.003096322],"study_design_scores_gemma":[0.00003782503,0.0006226711,0.98933,0.00002407357,0.0002683334,0.0006280798,0.000347009,0.007703131,0.0003678024,0.00006970562,0.0005837675,0.00001756406],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990396,0.0002149625,0.0003639942,0.00002319727,0.000003176342,0.00001442035,0.0002483676,0.000004260302,0.00008785587],"genre_scores_gemma":[0.9988704,0.0001625025,0.0002469777,0.00001425315,0.000008362456,0.00001255444,0.0006294115,0.000002913332,0.00005257706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004634331,"threshold_uncertainty_score":0.01041251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621300643374136,"score_gpt":0.3145116209872639,"score_spread":0.2982986145535225,"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."}}