{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T09:29:57Z","timestamp":1793179797367,"version":"4.5.0"},"reference-count":37,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clinical Hemorheology and Microcirculation"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>In practical clinical work, when sonographers extract image features, there may be large intra-observer and inter-observer variability in subjective description and visual evaluation. Lymph node tuberculosis is often confused with other diseases of lymphadenopathy. To avoid the shortcomings of ultrasound such as strong subjectivity and low repeatability, we discussed the clinical value of imaging models based on B-mode ultrasound (B-US), elastic ultrasound (EUS) and contrast-enhanced ultrasound (CEUS) images in predicting cervical lymph node tuberculosis (CLNT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Herein, 215 patients with cervical lymph node enlargement confirmed via international diagnostic criteria at our hospital between January 2018 and May 2023 were included. Patients were randomly divided into training (n\u2009=\u2009151) and validation (n\u2009=\u200964) sets in a 7:3 ratio. Thereafter, 42 patients with cervical lymphadenopathy who underwent ultrasound-guided lymph node puncture from March 2023 to September 2023 were considered as a prospective internal validation set. Three models (radiomics model, clinical model and clinical-radiomics model) were established. Receiver operating characteristic curves (ROCs) of different models were drawn, and the area under the curve (AUC),were compared among them. Finally, the visual color band nomogram was established.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The AUC of the clinical-radiomics model in the training dataset, validation dataset and prospective validation dataset reached 0.959, 0.906 and 0.865, respectively. The clinical-radiomics model has good diagnostic efficacy in predicting CLNT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The Multimodal ultrasound radiomics combined with clinical manifestations and imaging features, showed good judgment in identifying CLNT ability and good stability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13860291241304060","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T04:00:32Z","timestamp":1744862432000},"page":"324-334","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["To predict cervical lymph node tuberculosis based on clinical-multimodal ultrasound radiomics model"],"prefix":"10.1177","volume":"89","author":[{"given":"Ying","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, Zhejiang Provincial People's Hospital, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, Hangzhou Red Cross Hospital, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialei","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, The Second Clinical Medical College, Zhejiang Chinese Medicine University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Yan","sequence":"additional","affiliation":[{"name":"Division of Health Sciences, Hangzhou Normal University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, The Second Clinical Medical College, Zhejiang Chinese Medicine University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehui","family":"Yu","sequence":"additional","affiliation":[{"name":"Division of Health Sciences, Hangzhou Normal University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, Hangzhou Red Cross Hospital, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Global tuberculosis report 2022","author":"World Health Organization","year":"2022","unstructured":"World Health Organization. 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