Introduction: Intrafraction tumour motion in helical tomotherapy was investigated by comparing pre- and mid-fraction CT scans in patients with early non-small cell lung carcinoma (NSCLC) to assess the efficacy of a 7-mm margin around gross tumour volumes (GTVs) in stereotactic body radiation therapy (SBRT).
Methods: Thirty patients with early-stage NSCLC received SBRT in four or five fractions for a total of 141 treatments. A slow positron emission tomography/CT scan was fused with the simulation CT to determine the GTV. A planning target volume was created by placing an isotropic margin of 7 mm around the GTV. Data were retrospectively analyzed to assess translational tumour positional changes along the x, y and z axes and vector changes in millimeters from the pretreatment megavoltage (MV)-CT to the mid-fraction MV-CT.
Results: Average movements for all 141 treatment days along the x, y and z axes were 0.5 ± 2.3, -0.3 ± 3.0 and 0.9 ± 3.0 mm, respectively. Average movements for each patient along the x, y and z axes were 0.5 ± 1.5, -0.2 ± 2.0 and 0.9 ± 1.9 mm, respectively. Average vector displacement was 4.3 ± 2.4 mm for all treatment days and 4.2 ± 1.7 mm for each patient. Of 141 treatments, 137 (97.2%) fell within 7.0 mm in all axes.
Conclusion: The addition of a 7-mm margin to the GTV for patients receiving SBRT for NSCLC using tomotherapy is adequate to account for tumour movement. Mid-fraction CT scans proved to be valuable in assessing intrafraction tumour motion.
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http://dx.doi.org/10.1111/1754-9485.12179 | DOI Listing |
Phys Med
January 2025
Centre for Medical and Radiation Physics, University of Wollongong, NSW, Australia; St George Cancer Care Centre, St George Hospital, Kogarah, NSW, Australia; School of Physics, University of Sydney, Camperdown, NSW, Australia.
Purpose: Even with modern immobilisation devices, some amount of intrafraction patient motion is likely to occur during stereotactic radiosurgery (SRS) delivery. The aim of this work was to investigate how robustness of plans to intrafraction motion is affected by plan geometry and complexity.
Methods: In 2018, the Trans-Tasman Radiation Oncology Group conducted a multiple-target SRS international planning challenge, the data from which was utilised in this study.
Strahlenther Onkol
January 2025
Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs.
View Article and Find Full Text PDFClin Transl Radiat Oncol
March 2025
Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Purpose: To use imaging data from stereotactic MR-guided online adaptive radiotherapy (SMART) of ultracentral lung tumors (ULT) for development of a safe non-adaptive approach towards stereotactic body radiotherapy (SBRT) of ULT.
Patients And Methods: Analysis is based on 19 patients with ULT who received SMART (10 × 5.0-5.
Phys Med
January 2025
Department of Radiation Oncology, The Third Affiliated Hospital, Sun Yan-Sen University, Guangzhou 510630, China. Electronic address:
A preliminary study was conducted using electronic portal imaging device (EPID) based dose verification in pre-treatment and in vivo dose reconstruction modes for breast cancer intensity-modulated radiation therapy (IMRT) technique with known repositioning set-up errors. For 43 IMRT plans, the set-up errors were determined from 43 sets of EPID images and 258 sets of cone beam computed tomography images. In-house developed Edose software was used to reconstruct the dose distribution using the pre-treatment and on-treatment (in vivo) EPID acquired fluence maps.
View Article and Find Full Text PDFPhys Med Biol
January 2025
The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkynamicconstruction andotionstimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.
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