{"id":"W7117476274","doi":"10.21608/ijicis.2025.422365.1426","title":"Integrative Radio-Genomic Analysis of Lung Adenocarcinoma: Linking Imaging Features and Gene Expression Profiles for Tumor Classification","year":2025,"lang":"en","type":"article","venue":"International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Innovation Studies","funders":"","keywords":"Discriminative model; Transcriptome; Radiogenomics; Molecular imaging; Identification (biology); Radiomics; Gene; Adenocarcinoma","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":[0.0006765689,0.0004388188,0.0005290466,0.0009613324,0.0001865855,0.0006653893,0.0002714679,0.000264921,0.0007001327],"category_scores_gemma":[0.0009730588,0.0001843492,0.0005317316,0.0008166669,0.0002484572,0.0002425025,0.0004671835,0.0003628356,0.0003616195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002570194,"about_ca_system_score_gemma":0.0003482978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008654902,"about_ca_topic_score_gemma":0.002199593,"domain_scores_codex":[0.9997008,0.00005503954,0.00001676557,0.0001107334,0.00007457481,0.00004203006],"domain_scores_gemma":[0.999604,0.0001537137,0.0001028915,0.00004049065,0.00007504958,0.00002387102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004283405,0.000143342,0.09323007,0.0004115542,0.0002269439,0.0002213637,0.0002411119,0.006962494,0.7972745,0.0004947305,0.0005538853,0.09981165],"study_design_scores_gemma":[0.00003052545,0.0008010148,0.6191473,0.00008067751,0.0006390354,0.0009974552,0.000503958,0.1172574,0.2508104,0.002490985,0.00716622,0.00007493624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9150729,0.002442598,0.07671641,0.0002104767,0.00002195178,0.0001270373,0.002860595,0.0005263194,0.002021697],"genre_scores_gemma":[0.9523256,0.0006810359,0.04140854,0.0001149387,0.00001849319,0.0001256925,0.004296423,0.00007324119,0.0009559885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009613324,"threshold_uncertainty_score":0.003578126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509077507839285,"score_gpt":0.3445695806284315,"score_spread":0.3294788055500386,"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."}}