{"id":"W4391043510","doi":"10.14740/wjon1731","title":"Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma","year":2024,"lang":"en","type":"article","venue":"World Journal of Oncology","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Medicine; Radiomics; Hepatocellular carcinoma; Magnetic resonance imaging; Radiology; Receiver operating characteristic; Univariate; Logistic regression; Univariate analysis; Cohort; Multivariate analysis; Nuclear medicine; Multivariate statistics; Internal medicine; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009347373,0.0001920713,0.0006619638,0.0004513939,0.00004224785,0.00001039117,0.0002069942,0.00007791206,0.00003265893],"category_scores_gemma":[0.00008069739,0.0001281881,0.000252995,0.0005821891,0.0003358564,0.0001282708,0.0000595453,0.0005619075,0.000001561035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004458419,"about_ca_system_score_gemma":0.0009752904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001959288,"about_ca_topic_score_gemma":0.0001211919,"domain_scores_codex":[0.9981462,0.000253916,0.0008481719,0.0002246181,0.0002731594,0.0002538841],"domain_scores_gemma":[0.9988095,0.0002399073,0.000308954,0.0002418581,0.0003100412,0.00008977945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005682798,0.0002252955,0.878891,0.0002280613,0.00008090406,0.001169593,0.002855174,0.00001385393,0.07458341,0.00003247497,0.00005697671,0.04129496],"study_design_scores_gemma":[0.006920655,0.007350297,0.4439996,0.00211488,0.000978602,0.001863511,0.00084912,0.182239,0.3476957,0.00005299353,0.005661691,0.0002738936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9430519,0.05486399,0.00006640214,0.0009233419,0.0002820744,0.0006146599,0.0000144912,0.000006381521,0.000176704],"genre_scores_gemma":[0.9961217,0.001576766,0.001870572,0.00008172359,0.0001042902,0.00002238576,0.000004866808,0.00002371635,0.0001939591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4348914,"threshold_uncertainty_score":0.5227358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03941926077653529,"score_gpt":0.2696448813627793,"score_spread":0.230225620586244,"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."}}