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The study aimed to develop a method based on the two\u2010dimensional shear wave elastography and clinical data to evaluate the risk of PHLF in HCC patients with chronic hepatitis B.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This multicenter study proposed a deep learning model (PHLF\u2010Net) incorporating dual\u2010modal ultrasound features and clinical indicators to predict the PHLF risk. The datasets were divided into a training cohort, an internal validation cohort, an internal independent testing cohort, and three external independent testing cohorts. Based on ResNet50 pretrained on ImageNet, PHLF\u2010Net used a progressive training strategy with images of varying granularity and incorporated conventional B\u2010mode and elastography images and clinical indicators related to liver reserve function.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 532 HCC patients who underwent hepatectomy at five hospitals were enrolled. PHLF occurred in 147 patients (27.6%, 147\/532). The PHLF\u2010Net combining dual\u2010modal ultrasound and clinical indicators demonstrated high effectiveness for predicting PHLF, with AUCs of 0.957 and 0.923 in the internal validation and testing sets, and AUCs of 0.950, 0.860, and 1.000 in the other three independent external testing sets. The performance of PHLF\u2010Net outperformed models of single\u2010 and dual\u2010modal US.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Preoperative ultrasound imaging combining clinical indicators can effectively predict the PHLF probability in patients with HCC. In the internal and external validation sets, PHLF\u2010Net demonstrated its usefulness in predicting PHLF.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jum.16559","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T11:57:40Z","timestamp":1724414260000},"page":"2269-2280","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma"],"prefix":"10.1002","volume":"43","author":[{"given":"Liyun","family":"Xue","sequence":"first","affiliation":[{"name":"Department of Ultrasound Huashan Hospital, Fudan University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juncheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Fudan University  Shanghai 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