Background: Intensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.
Methods: Our current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times.
Results: Average V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%.
Conclusions: Using the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans.
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http://dx.doi.org/10.1186/s13014-015-0388-6 | DOI Listing |
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
College of Nursing, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
Background: TheKeep.Ca was built to facilitate engagement with those experiencing cancer in Manitoba, Canada. Constructed between 2020 and 2024 with a group of patient advisors, the website includes information on engagement activities including research participation, the patient advisor role, and how those experiencing cancer can access these Manitoba activities.
View Article and Find Full Text PDFJ Clin Oncol
January 2025
Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Chinese University of China, Shatin, Hong Kong Special Administrative Region, China.
Purpose: Mobocertinib is an oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that targets exon 20 insertion (ex20ins) mutations in non-small cell lung cancer (NSCLC). This open-label, phase III trial (EXCLAIM-2: ClinicalTrials.gov identifier: NCT04129502) compared mobocertinib versus platinum-based chemotherapy as first-line treatment of ex20ins+ advanced/metastatic NSCLC.
View Article and Find Full Text PDFAnnu Rev Clin Psychol
January 2025
Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; email:
The opioid crisis, driven by illicitly manufactured fentanyl, presents significant challenges in treating opioid use disorder (OUD) and opioid withdrawal syndrome. Fentanyl is uniquely lethal due to its rapid onset and respiratory depressant effects, driving the surge in overdose deaths. This review examines the limitations of traditional diagnostic criteria like those of the , Fifth Edition, Text Revision (DSM-5-TR) and explores the potential of dimensional models such as the Hierarchical Taxonomy of Psychopathology (HiTOP) for a more nuanced understanding of OUD.
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