Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures.
Purpose: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic.
Methods: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use.
Results: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively.
Conclusion: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/mp.16806 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
SPACE: STRING proteins as complementary embeddings.
Bioinformatics
September 2025
Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
Motivation: Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting.
Results: We leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings.
Enhancing Team Strategies and Tools to Enhance Performance and Patient Safety Performance Through Medical Movies, Massive Open Online Courses, and 3D Virtual Simulation-Based Interprofessional Education: Mixed Methods Double-Blind Quasi-Experimental Study.
J Med Internet Res
September 2025
Chulalongkorn University, Bangkok, Thailand.
Background: The interprofessional educational curriculum for patient and personnel safety is of critical importance, especially in the context of the COVID-19 pandemic, to prepare junior multiprofessional teams for emergency settings.
Objective: This study aimed to evaluate the effectiveness of an innovative interprofessional educational curriculum that integrated medical movies, massive open online courses (MOOCs), and 3D computer-based or virtual reality (VR) simulation-based interprofessional education (SimBIE) with team co-debriefing to enhance interprofessional collaboration and team performance using Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS). This study addressed 3 key questions.
The Art of the Special Interest: Lexicon-Based Analysis of Longitudinal Changes in Language Patterns Among Neurodiverse Youth Designing Special Interest-Related Digital Art.
J Med Internet Res
September 2025
Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States.
We estimated linear mixed-effects models to analyze changes in language patterns (as measured using Linguistic Inquiry and Word Count) among neurodiverse youth to introduce a novel assessment useful for research into the potential benefits of special interests while minimizing respondent and researcher burden.
View Article and Find Full Text PDFMyo-inositol Supplementation to Prevent Pregnancy Complications in Polycystic Ovary Syndrome: A Randomized Clinical Trial.
JAMA
September 2025
Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, the Netherlands.
Importance: Pregnant individuals with polycystic ovary syndrome (PCOS) present with a higher risk of pregnancy complications, including gestational diabetes, preeclampsia, and preterm birth. Myo-inositol supplementation may reduce these risks.
Objective: To determine whether daily supplementation with myo-inositol during pregnancy among individuals with PCOS reduces the risk of a composite outcome of gestational diabetes, preeclampsia, and preterm birth.
Adaptive radiotherapy dose prediction on head and neck cancer patients with a 3D multi-headed U-Net deep learning architecture.
Mach Learn Health
December 2025
Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
Online adaptive radiation therapy (ART) personalizes treatment plans by accounting for daily anatomical changes, requiring workflows distinct from conventional radiotherapy. Deep learning-based dose prediction models can enhance treatment planning efficiency by rapidly generating accuracy dose distributions, reducing manual trial-and-error and accelerating the overall workflow; however, most existing approaches overlook critical pre-treatment plan information-specifically, physician-defined clinical objectives tailored to individual patients. To address this limitation, we introduce the multi-headed U-Net (MHU-Net), a novel architecture that explicitly incorporates physician intent from pre-treatment plans to improve dose prediction accuracy in adaptive head and neck cancer treatments.
View Article and Find Full Text PDF