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This method can predict the anatomical changes of a patient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone\u2010beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short\u2010term memory network\u2010generative adversarial network (LSTM\u2010GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3\u20136 and named LSTM\u2010GAN\u2010week 3 to LSTM\u2010GAN\u2010week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5\u00a0Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6\u00a0Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8\u00a0Gy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17381","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T13:51:05Z","timestamp":1725371465000},"page":"7998-8009","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Bining","family":"Yang","sequence":"first","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing 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