{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T18:48:29Z","timestamp":1789325309563,"version":"build-2803163510"},"reference-count":35,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["P0056738"],"award-info":[{"award-number":["P0056738"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Science Progress"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to develop and compare radiomics signatures derived from different renal regions on ultrasound images to assess fibrosis severity in chronic kidney disease (CKD) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 146 CKD patients who underwent renal ultrasound and biopsy were enrolled. Radiomics features were extracted from the whole kidney, parenchyma, and mid-portion to generate region-specific signatures: radscore_whole, radscore_parenchyma, and radscore_mid-portion. Diagnostic performance in distinguishing mild from moderate-to-severe fibrosis was evaluated using receiver operating characteristic (ROC) curve analysis. Performance improvements were assessed via net reclassification improvement (NRI) and integrated discrimination improvement (IDI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The radscore_mid-portion displayed the highest discriminatory accuracy, yielding an area under the ROC curve (AUC) of 0.74 (95% CI: 0.65\u20130.82), which exceeded both the radscore_whole (AUC\u2009=\u20090.61, 95% CI: 0.51\u20130.70;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.035) and radscore_parenchyma (AUC\u2009=\u20090.66, 95% CI: 0.56\u20130.74;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.181). Reclassification analysis confirmed the added diagnostic value of the mid-portion signature, with significant improvements compared with both the whole-kidney (NRI\u2009=\u200936.35%; IDI\u2009=\u200910.57%) and parenchyma signatures (NRI\u2009=\u200942.23%; IDI\u2009=\u200910.42%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics signatures from different renal regions offer varying diagnostic utility. The mid-portion-based signature demonstrated improved performance and added value in identifying moderate-to-severe renal fibrosis in CKD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00368504251399597","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T19:11:41Z","timestamp":1764011501000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Renal fibrosis assessment in chronic kidney disease: Exploring varied kidney regions with ultrasound-based radiomics analysis"],"prefix":"10.1177","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2765-5002","authenticated-orcid":false,"given":"Ziman","family":"Chen","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingli","family":"Wang","sequence":"additional","affiliation":[{"name":"Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Wu","sequence":"additional","affiliation":[{"name":"The Fifth Affiliated Hospital of Sun Yat-sen University"},{"name":"The First Affiliated Hospital of Wenzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30045-3"},{"key":"e_1_3_3_3_2","article-title":"China Kidney Disease Network (CK-NET) 2016 annual data report","volume":"10","author":"Zhang L","year":"2011","unstructured":"Zhang L, Zhao MH, Zuo L, et al. 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Texture analysis of ultrasound images of chronic kidney disease. 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), 2017, pp. 1\u20135."},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.21037\/qims-22-939"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-158-11-201306040-00007"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-147-8-200710160-00010"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-021-03351-x"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.01646"},{"key":"e_1_3_3_25_2","first-page":"576","article-title":"Deep learning radiomics model accurately predicts hepatocellular carcinoma occurrence in chronic hepatitis B patients: a five-year follow-up","volume":"11","author":"Jin J","year":"2021","unstructured":"Jin J, Yao Z, Zhang T, et al. 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