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Purpose: Stereotactic body radiotherapy (SBRT) is an effective treatment for adrenal gland metastases, but it is technically challenging and there are concerns about toxicity. We performed a multi-institutional pooled retrospective analysis to study clinical outcomes and toxicities after MR-guided SBRT (MRgSBRT) using for adrenal gland metastases. Methods and Materials: Clinical and dosimetric data of patients treated with MRgSBRT on a 0.35 T MR-Linac at 11 institutions between 2016 and 2022 were analyzed. Local control (LC), local progression-free survival (LPFS), distant progression-free survival (DPFS) and overall survival (OS) were estimated using Kaplan-Meier method and log-rank test. Results: A total of 255 patients (269 adrenal metastases) were included. Metastatic pattern was solitary in 25.9 % and oligometastatic in 58.0 % of patients. Median total dose was 45 Gy (range, 16–60 Gy) in a median of 5 fractions, and the median BED10 was 100 Gy (range, 37.5–132.0 Gy). Adaptation was done in 87.4 % of delivered fractions based on the individual clinicians' judgement. The 1- and 2- year LPFS rates were 94.0 % (95 % CI: 90.7–97.3 %) and 88.3 % (95 % CI: 82.4–94.2 %), respectively and only 2 patients (0.8 %) experienced grade 3 + toxicity. No local recurrences were observed after treatment to a total dose of BED10 > 100 Gy, with single fraction or fractional dose of > 10 Gy. Conclusions: This is a large retrospective multi-institutional study to evaluate the treatment outcomes and toxicities with MRgSBRT in over 250 patients, demonstrating the need for frequent adaptation in 87.4 % of delivered fractions to achieve a 1- year LPFS rate of 94 % and less than 1 % rate of grade 3 + toxicity. Outcomes analysis in 269 adrenal lesions revealed improved outcomes with delivery of a BED10 > 100 Gy, use of single fraction SBRT and with fraction doses > 10 Gy, providing benchmarks for future clinical trials.

Abstract Objective To review our experience with a standardized dental management approach in patients with planned radiotherapy of the head and neck region based on preradiation and follow‐up data. Material and Methods Records of patients who underwent radiotherapy between June 2016 and November 2020 were reviewed. Data on dental findings and therapeutic recommendations were extracted from a prospectively managed database. Hospital records were used to obtain follow‐up data. Results Two hundred eighty‐one patient records were identified. After the exclusion of 81 patients because of incomplete data, 200 patients were included in the study. Dental findings relevant to radiotherapy were found in 144 cases (72.0%). Teeth extractions were recommended in 112 (56.0%) patients. Follow‐up data were available for 172 (86.0%) patients (mean follow‐up: 16.8 ± 10.7 months). Radiodermatitis was the most frequently observed sequela of radiotherapy (42.4%), followed by dysphagia (38.4%) and stomatitis (36.6%). Osteoradionecrosis was observed in only 2.3% of the patients. Conclusion Dental findings relevant to planned radiotherapy were frequent and in many cases resulted in recommendations for teeth extraction. Based on our standardized dental management protocol, we observed low rates of late oral complications after radiotherapy of the head and neck region.

This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.