{"doi":"10.1016/j.jcct.2025.01.007","title":"Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans","abstract":"BACKGROUND: Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS). METHODS: This study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0-3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net logistic regression and used to train a CatBoost model. Model performance was evaluated using 1000 repeated five-fold cross-validation and survival analyses to predict major adverse cardiovascular event (MACE) and revascularization. Generalizability was assessed using an external validation set of 2316 patients for survival predictions. RESULTS: Among the 1324 patients, obstructive CAD was identified in 334 patients (25.2 ​%). Elastic net regression identified the top 14 features (5 clinical, 2 Agatston-score-derived, and 7 fat-omics). The proposed method achieved excellent performance for classifying obstructive CAD, with an AUC of 90.1 ​± ​0.9 ​% and sensitivity/specificity/accuracy of 83.5 ​± ​5.5 ​%/93.7 ​± ​1.9 ​%/82.4 ​± ​2.0 ​%. The inclusion of Agatston-score-derived and fat-omics features significantly improved classification performance. Survival analyses showed that both actual and predicted obstructive CAD significantly differentiated patients who experienced MACE and revascularization. CONCLUSIONS: We developed a novel machine learning model to predict obstructive CAD from non-contrast CTCS scans. Our findings highlight the potential clinical benefits of CTCS imaging in identifying patients likely to benefit from advanced imaging.","journal":"Journal of cardiovascular computed tomography","year":2025,"id":518984,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9626,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1050994,"name":"Tao Hu","orcid":"0000-0001-5267-7804","position":1,"is_corresponding":false},{"id":235799,"name":"Michelle C. Williams","orcid":"0000-0003-3556-2428","position":2,"is_corresponding":false},{"id":829571,"name":"Ammar Hoori","orcid":"0000-0002-8458-1214","position":3,"is_corresponding":false},{"id":1132465,"name":"Hao Wu","orcid":"0000-0001-6993-8863","position":4,"is_corresponding":false},{"id":323658,"name":"Justin N. Kim","orcid":"0000-0003-4713-8552","position":5,"is_corresponding":false},{"id":259172,"name":"David E. Newby","orcid":"0000-0001-7971-4628","position":6,"is_corresponding":false},{"id":264737,"name":"Robert Gilkeson","orcid":"0000-0002-5931-8123","position":7,"is_corresponding":false},{"id":103416,"name":"Sanjay Rajagopalan","orcid":"0000-0001-6669-8163","position":8,"is_corresponding":false},{"id":311212,"name":"David L. Wilson","orcid":"0000-0001-9763-1463","position":9,"is_corresponding":false},{"id":1386888,"name":"Ju Hwan Lee","orcid":"0000-0001-6762-4588","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:49:14.184995Z","pmid":"39909764","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}