Background: Higher isodose lines (IDLs) in Gamma Knife (GK) Perfexion treatment of brain metastases (BMet) could result in lower local control (LC) or higher radiation necrosis (RN) rates, but reduce treatment time.
Objective: To assess the impact of the heterogeneity index (HI) and conformality index (CFI) ion local failure (LF) for patients treated with GK for 1 to 3 BMet.
Methods: From an institutional review board-approved database, 320 patients with 496 BMet were identified, treated for 1 to 3 BMet from July 2007 to April 2011 on GK Perfexion. Cox proportional hazards regression was used to analyze significance of HI, CFI, IDL, dose, tumor diameter, recursive partitioning analysis class, tumor radioresistance, primary, smoking history, metastasis location, and whole-brain radiation therapy (WBRT) history with LF and RN.
Results: Median follow-up by lesion was 6.8 months (range, 0-49.6). The series median survival was 14.2 months. Per RECIST, 9.5% of lesions failed, 33.9% were stable, 38.3% partially responded, 17.1% responded completely, and 1.2% could not be assessed. The 12-month LC rate was 87.3%. On univariate analysis, a dose less than 20 Gy (hazard ratio [HR]: 2.940, P < .001); tumor size (HR: 1.674, P < .001); and cerebellum/brainstem location vs other (HR: 1.891, P = .043) were significant for LF. Non-small cell lung cancer (HR: 0.333, P = .0097) was associated with better LC. On multivariate analysis, tumor size (HR: 1.696, P < .001) and cerebellum/brainstem location vs other (HR: 1.959, P = .033) remained significant for LF. Variables not significant for LF included CI, IDL, and HI.
Conclusion: Our study of patients with 1 to 3 BMet treated with GK demonstrated no difference in LC or RN with varying HI, indicating that physicians can treat to IDL at 70% or higher IDL to reduce treatment time without increased LF or RN.
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http://dx.doi.org/10.1227/NEU.0000000000000289 | DOI Listing |
Radiat Oncol
November 2024
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients.
View Article and Find Full Text PDFRadiography (Lond)
October 2024
Department of Radiation Oncology, Balco Medical Centre, New Raipur, Chhattisgarh, India.
Introduction: This study investigates dosimetric influence and gradient-analysis of leaf-width of various Multi-leaf-collimators (MLC) from Truebeam and Halcyon linear accelerators.
Methods: The leaf-width effects of Millennium120 and HD120MLC from Truebeam and SX1 and SX2 from Halcyon were studied using virtual phantom in Eclipse16.1.
J Pak Med Assoc
October 2024
Ghazi Al-Harriri Hospital for Specialist Surgeries, Baghdad Medical City, Ministry of Health, Baghdad, Iraq.
Front Nucl Med
October 2023
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
Cureus
July 2024
Department of Radiology, Aichi Medical University, Nagakute, JPN.
Introduction In stereotactic radiosurgery (SRS) for brain metastasis (BM), the target dose inhomogeneity remains highly variable among modalities, irradiation techniques, and facilities, which can affect tumor response during and after multi-fraction SRS. Volumetric-modulated arcs (VMAs) can provide a concentrically-layered steep dose increase inside a gross tumor volume (GTV) boundary compared to dynamic conformal arcs. This study was conducted to review the optimal evaluation method for the internal GTV doses relevant to maximal response and local control, specifically to examine the significance of the doses 2 mm and 4 mm inside the GTV boundary in VMA-based SRS.
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