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Background And Objective: Radiation-induced cystitis (RIC) is an important consequence of pelvic radiotherapy that can cause high morbidity and, in extreme cases, mortality. The lack of a widely accepted classification system makes it difficult to compare treatment regimens. Our aim was to develop a new classification system covering the RIC spectrum to improve treatment comparisons and accurate incidence estimates for systematic use in clinical and research settings.

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Introduction: Brain arteriovenous malformations (AVM) are complex vascular pathologies with a significant risk of hemorrhage. Stereotactic radiosurgery (SRS) is an effective treatment modality for AVM, initially popularized on the Gamma Knife (Elekta AB, Stockholm, Sweden) platform, and now benefits from the modern advances in linear accelerator (LINAC)-based platforms. This study evaluates the outcomes of LINAC-based SRS/hypofractionated stereotactic radiotherapy (hFSRT) for cerebral AVMs.

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Objectives: This study aimed to investigate the association between the type of prostate biopsy and the occurrence of rectal wall infiltration (RWI) with hydrogel spacer placement in patients undergoing radiotherapy for prostate cancer.

Methods: A retrospective study was conducted involving 175 patients who received hydrogel spacer placement before radiotherapy at the National Cancer Center East Hospital, between October 2021 and December 2023. The patients were categorized based on the type of prostate biopsy: transperineal and transrectal.

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Purpose: Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics.

Methods: This study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net +  + deep learning model for generating synthetic CT (sCT).

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MR-linac: role of artificial intelligence and automation.

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.

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