{"doi":"10.1117/12.2654412","title":"Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images","abstract":"of 8.02 mGy. Five types of images: virtual monoenergetic images (VMIs) at 50-keV, 70-keV, and 100-keV, iodine maps, and virtual non-contrast (VNC) images were reconstructed using an iterative reconstruction algorithm (QIR), a quantitative kernel (Qr40) and 0.6-mm/0.3-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 93 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Two significant and non-redundant features were input into a support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the classification accuracy was computed. Fifteen low-risk and ten high-risk plaques were identified by the radiologist. A total of 18, 32, 43, 16, and 55 out of 93 features in 50-keV, 70-keV, 100-keV, iodine map, and VNC images were statistically significant. A total of 17, 19, 22, 20, and 22 out of 25 plaques were classified correctly in 50-keV, 70-keV, 100-keV, iodine map, and VNC images, respectively. A ML model using 100-keV VMIs and VNC images derived from coronary PCD-CTA best automatically differentiated low- and high-risk coronary plaques.","journal":"PubMed","year":2023,"id":390362,"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":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8632,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":264736,"name":"Prabhakar Rajiah","orcid":"0000-0001-7538-385X","position":1,"is_corresponding":false},{"id":254230,"name":"Scott S. Hsieh","orcid":"0000-0003-3111-7001","position":2,"is_corresponding":false},{"id":1161712,"name":"Andrea Esquivel","orcid":"0000-0002-5260-4661","position":3,"is_corresponding":false},{"id":1119143,"name":"Mariana Yalon","orcid":"0000-0001-7032-4068","position":4,"is_corresponding":false},{"id":1033640,"name":"Nikkole M. Weber","orcid":"0000-0001-6198-2958","position":5,"is_corresponding":false},{"id":355872,"name":"Hao Gong","orcid":"0000-0002-1123-7172","position":6,"is_corresponding":false},{"id":237064,"name":"Joel G. Fletcher","orcid":"0000-0002-8941-5434","position":7,"is_corresponding":false},{"id":237065,"name":"Cynthia H. McCollough","orcid":"0000-0002-5346-332X","position":8,"is_corresponding":false},{"id":237063,"name":"Shuai Leng","orcid":"0000-0002-6453-9481","position":9,"is_corresponding":false},{"id":1161711,"name":"Chelsea A. S. Dunning","orcid":"0000-0002-3252-8817","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T01:18:32.854511Z","pmid":"37064414","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":[]}