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Artificial intelligence (AI) is playing an important role in radiation oncology. One of the most important applications is in radiotherapy physics. In this field, it has improved the automation of radiotherapy plan design and quality control (QC), thereby promoting and ensuring individualized precision treatment. This article reviews the applications and research on AI in the physics of radiotherapy and projects the prospects of AI in the following aspects: radiotherapy plan design, radiotherapy quality assurance, and QC, organs at risk contouring, dose prediction, etc.
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http://dx.doi.org/10.4103/jcrt.jcrt_1438_21 | DOI Listing |
J Nurs Scholarsh
March 2025
Journal of Nursing Scholarship, Boston, MA, USA.
Transplant Proc
March 2025
Department of Urology, Singapore General Hospital, Singapore, Singapore. Electronic address:
Background And Objective: Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to process human language. As most human interactions today involve the internet and electronic devices, NLP tools quickly become indispensable to modern life. The use of NLP tools in medical practice and research is growing fast.
View Article and Find Full Text PDFHPB (Oxford)
March 2025
Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Background: Laparoscopic repeat liver resection (LRLR) is still a challenging technique and requires a careful selection of indications. However, the current difficulty scoring system is not suitable for selecting indications. The purpose of this study is to develop the indication model for LRLR using machine learning and to identify factors associated with open conversion (OC).
View Article and Find Full Text PDFJ Natl Med Assoc
February 2025
Editor-in-Chief, Journal of the National Medical Association, Professor of Medicine, George Washington University, Washington, D.C.
J Immunother Cancer
March 2025
Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Background: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develop and validate a deep learning model using a novel voxel-level radiomics approach to predict pCR based on preoperative CT images.
Methods: In this multicenter, retrospective study, 741 patients with ESCC who underwent nICT followed by radical esophagectomy were enrolled from three institutions.
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