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Therapeutic dose prediction using score-based diffusion model for pretreatment patient-specific quality assurance.
Front Oncol
January 2025
Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objectives: Implementing pre-treatment patient-specific quality assurance (prePSQA) for cancer patients is a necessary but time-consuming task, imposing a significant workload on medical physicists. Currently, the prediction methods used for prePSQA fall under the category of supervised learning, limiting their generalization ability and resulting in poor performance on new data. In the context of this work, the limitation of traditional supervised models was broken by proposing a conditional generation method utilizing unsupervised diffusion model.
View Article and Find Full Text PDFDeveloping an automatic treatment record review system for quality assurance of patient treatment delivery in radiation therapy.
Radiat Oncol
January 2025
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Background And Purpose: Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists.
View Article and Find Full Text PDFProbing the physical hallmarks of cancer.
Nat Methods
January 2025
Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
The physical microenvironment plays a crucial role in tumor development, progression, metastasis and treatment. Recently, we proposed four physical hallmarks of cancer, with distinct origins and consequences, to characterize abnormalities in the physical tumor microenvironment: (1) elevated compressive-tensile solid stresses, (2) elevated interstitial fluid pressure and the resulting interstitial fluid flow, (3) altered material properties (for example, increased tissue stiffness) and (4) altered physical micro-architecture. As this emerging field of physical oncology is being advanced by tumor biologists, cell and developmental biologists, engineers, physicists and oncologists, there is a critical need for model systems and measurement tools to mechanistically probe these physical hallmarks.
View Article and Find Full Text PDFPurpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models.
Methods: A set of 100 multiple choice radiation oncology physics questions, previously created by a well-experienced physicist, was used for this study. The answer options of the questions were randomly shuffled to create "new" exam sets.
Clinical Application Of Deep Learning-assisted Needles Reconstruction In Prostate Ultrasound Brachytherapy.
Int J Radiat Oncol Biol Phys
January 2025
McGill university, Montreal, Qc, Canada.
Purpose: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning.
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