98%
921
2 minutes
20
Purpose: For LINAC-based stereotactic radiosurgery (SRS) treatments, the binary MLC models utilizing single dosimetric leaf gap (DLG) parameters in Eclipse versions prior to v18 can result in imperfect agreement between measured and calculated doses, increased commissioning complexity, and user-dependent variability. This study aims to evaluate the efficiency and accuracy of the enhanced leaf model (ELM) in Eclipse version 18.0, which incorporates the actual rounded-end MLC design in dose calculations.
Methods: ELM parameters were determined from measurements and configuration in a test Eclipse v18.0 system for an Edge LINAC with High Definition MLC (HDMLC) and a TrueBeam LINAC with the Millennium 120-leaf MLC (M120). The anisotropic analytical algorithm (AAA) was used to calculate doses for both 10FFF and 6FFF energies. The v18 ELM model was compared to the current version 16.1 (v16) model, which utilized single DLG parameter. Dose calculations were performed and compared for (1) static small on-axis fields, (2) static small off-axis fields, (3) single-isocenter single-target (SIST) HyperArc plans, and (4) single-isocenter multiple-target (SIMT) HyperArc plans. Gafchromic EBT4 film and myQA SRS device were used for dose verification.
Results: The measurement required for ELM was similar to that of the original DLG, but ELM configuration provided significant time savings. The measurements showed comparable or improved accuracy with the ELM model for both static fields and patient-specific plans. A significant improvement in dose calculation accuracy was observed with the ELM particularly for SIMT patients with a large number of small targets.
Conclusion: The new ELM introduced in Eclipse v18 substantially improves efficiency and consistency of the modeling process in the Eclipse dose calculation algorithm while maintaining comparable or superior accuracy for Linac-based SRS.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12256662 | PMC |
http://dx.doi.org/10.1002/acm2.70143 | DOI Listing |
JMIR Res Protoc
September 2025
Department of Urology, Faculty of Medicine, Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Background: Circumcision is a widely practiced procedure with cultural and medical significance. However, certain penile abnormalities-such as hypospadias or webbed penis-may contraindicate the procedure and require specialized care. In low-resource settings, limited access to pediatric urologists often leads to missed or delayed diagnoses.
View Article and Find Full Text PDFJ Craniofac Surg
September 2025
Department of Oral and Maxillofacial Surgery, University of Ulsan Hospital, University of Ulsan College of Medicine.
This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma.
View Article and Find Full Text PDFJ Craniofac Surg
September 2025
Department of Breast Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China.
Background: With the development of artificial intelligence, obtaining patient-centered medical information through large language models (LLMs) is crucial for patient education. However, existing digital resources in online health care have heterogeneous quality, and the reliability and readability of content generated by various AI models need to be evaluated to meet the needs of patients with different levels of cultural literacy.
Objective: This study aims to compare the accuracy and readability of different LLMs in providing medical information related to gynecomastia, and explore the most promising science education tools in practical clinical applications.
JCO Clin Cancer Inform
September 2025
USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA.
Purpose: To evaluate a generative artificial intelligence (GAI) framework for creating readable lay abstracts and summaries (LASs) of urologic oncology research, while maintaining accuracy, completeness, and clarity, for the purpose of assessing their comprehension and perception among patients and caregivers.
Methods: Forty original abstracts (OAs) on prostate, bladder, kidney, and testis cancers from leading journals were selected. LASs were generated using a free GAI tool, with three versions per abstract for consistency.
JMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDF