{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T13:38:49Z","timestamp":1783604329212,"version":"3.55.0"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Ultrasound"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Predicting tumor regression grade (TRG) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC) preoperatively accurately is crucial for providing individualized treatment plans. This study aims to develop transrectal contrast\u2010enhanced ultrasound\u2010based (TR\u2010CEUS) radiomics models for predicting TRG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 190 LARC patients undergoing NCRT and subsequent total mesorectal excision were categorized into good and poor response groups based on pathological TRG. TR\u2010CEUS examinations were conducted before and after NCRT. Machine learning (ML) models for predicting TRG were developed by employing pre\u2010 and post\u2010NCRT TR\u2010CEUS image series, based on seven classifiers, including random forest (RF), multi\u2010layer perceptron (MLP) and so on. The predictive performance of models was evaluated using receiver operating characteristic curve analysis and Delong test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 1525 TR\u2010CEUS images were included for analysis, and 3360 ML models were constructed using image series before and after NCRT, respectively. The optimal pre\u2010NCRT ML model, constructed from imaging series before NCRT, was RF; whereas the optimal post\u2010NCRT model, derived from imaging series after NCRT, was MLP. The areas under the curve for the optimal RF and MLP models demonstrated values of 0.609 and 0.857, respectively, in the cross\u2010validation cohort, with corresponding values of 0.659 and 0.841 observed in the independent test cohort. Delong tests showed that the predictive efficacy of the post\u2010NCRT model was statistically higher than that of the pre\u2010NCRT model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics model developed by TR\u2010CEUS images after NCRT demonstrated high predictive performance for TRG, thereby facilitating precise evaluation of therapeutic response to NCRT in LARC patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcu.70071","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T08:55:22Z","timestamp":1757062522000},"page":"304-312","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Efficacy of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer Using Transrectal Contrast\u2010Enhanced Ultrasound\u2010Based Radiomics Model"],"prefix":"10.1002","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6485-5585","authenticated-orcid":false,"given":"Zhongfan","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Medical Ultrasound West China Hospital, Sichuan University  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Medical Ultrasound West China Hospital, Sichuan University  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Medical Ultrasound West China Hospital, Sichuan University  Chengdu 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