{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:09:19Z","timestamp":1760242159685,"version":"build-2065373602"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"1174","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Scientific and Technological Innovation"},{"DOI":"10.13039\/501100005892","name":"China Academy of Chinese Medical Sciences","doi-asserted-by":"publisher","award":["CI2021A03302"],"award-info":[{"award-number":["CI2021A03302"]}],"id":[{"id":"10.13039\/501100005892","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing United Imaging Research Institute of Intelligent Imaging Foundation","award":["CRIBJQY202109"],"award-info":[{"award-number":["CRIBJQY202109"]}]},{"name":"Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences","award":["CI2021A03320"],"award-info":[{"award-number":["CI2021A03320"]}]},{"name":"The Escort Project of Guang\u2019anmen Hospital"},{"name":"China Academy of Chinese Medical Science-Backbone Talent Cultivation","award":["9323015"],"award-info":[{"award-number":["9323015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To explore the predictive value of baseline CT radiomics for the 6-month and 12-month treatment efficacy of the Jianpibushen Prescription in femoral head necrosis (FHN), with the goal of optimizing treatment strategies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Retrospectively, ARCO stage 2-4 FHN patients who underwent hip joint CT scans before receiving Jianpibushen Prescription treatment from September 2016 to December 2023 were collected. 315 patients (M\/F\u2009=\u2009210\/105, median age 39.0\u2009years) were included. A total of 1928 radiomics features were extracted, downscaled and filtered. Finally, features were selected to construct the radiomics predictive model of the efficacy at 6 and 12\u2009months.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>For predicting the treatment efficacy at 6\u2009months, 8 features were selected to build model using Bootstrap Aggregating Decision Tree (Bagging). The model attained an area under curve (AUC) of 0.999 (0.997-1.0) in the training set and 0.736 (0.638-0.834) in the validation set. For predicting the 12-month treatment efficacy, a comparable radiomics model was constructed with Random Forest, with AUCs of 0.995 (0.991-0.999) in the training set and 0.783 (0.676-0.89) in the validation set.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Baseline CT radiomics features can relatively accurately predict the 6-month and 12-month efficacy of Jianpibushen Prescription, thus facilitating individualized and precise clinical treatment.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge<\/jats:title>\n                  <jats:p>For the first time, this study established a relatively accurate prediction model for the 6-month and 12-month efficacy of the Jianpibushen Prescription on FHN, based on baseline CT radiomics features, thus optimizing treatment strategies.<\/jats:p>\n               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