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Applying aperture-based intensity map in automated plan review of volumetric modulated arc therapy for lung cancer patients.
Quant Imaging Med Surg
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, China.
Background: Volumetric modulated arc therapy (VMAT) is a popular radiotherapy technique in the clinic. As consisting of hundreds of control points in a VMAT plan it is more complex and time consuming than those conventional treatment modalities, such as intensity modulated radiation therapy. To improve the efficiency and accuracy of its quality assurance procedure, a novel automated anomaly detection method was proposed.
View Article and Find Full Text PDFBackground: In proton radiotherapy, the steep dose deposition profile near the end of the proton's track, the Bragg peak, ensures a more conformed deposition of dose to the tumor region when compared with conventional radiotherapy while reducing the probability of normal tissue complications. However, uncertainties, as in the proton range, patient geometry, and positioning pose challenges to the precise and secure delivery of the treatment plan (TP). In vivo range determination and dose distribution are pivotal for mitigation of uncertainties, opening the possibility to reduce uncertainty margins and for adaptation of the TP.
View Article and Find Full Text PDFEvaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.
J Appl Clin Med Phys
January 2025
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Background: Accurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.
View Article and Find Full Text PDFAutomated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFA rare case of recurrent adenoid cystic carcinoma of the breast with weak hormone receptor positivity.
Radiol Case Rep
March 2025
Longstreet Clinic, Breast Surgery, 725 Jesse Jewell Parkway, Gainesville, GA 30501 USA.
Adenoid cystic carcinoma (ACC) of the breast is an exceptionally rare malignancy, accounting for less than 0.1% of all breast cancers. Despite its favorable prognosis, optimal management remains undefined due to its rarity and lack of consensus guidelines.
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