Background: The safe use of radiotherapy (RT) requires compliance with dose/volume constraints (DVCs) for organs at risk (OaRs). However, the available recommendations are sometimes conflicting and scattered across a number of different documents. Therefore, the aim of this work is to provide, in a single document, practical indications on DVCs for OaRs in external beam RT available in the literature. Material and Methods: A multidisciplinary team collected bibliographic information on the anatomical definition of OaRs, on the imaging methods needed for their definition, and on DVCs in general and in specific settings (curative RT of Hodgkin’s lymphomas, postoperative RT of breast tumors, curative RT of pediatric cancers, stereotactic ablative RT of ventricular arrythmia). The information provided in terms of DVCs was graded based on levels of evidence. Results: Over 650 papers/documents/websites were examined. The search results, together with the levels of evidence, are presented in tabular form. Conclusions: A working tool, based on collected guidelines on DVCs in different settings, is provided to help in daily clinical practice of RT departments. This could be a first step for further optimizations.

BackgroundA CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted.MethodsAt least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool.ResultsSeven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p<0.001). The DL-tool was generally appreciated by ROs, with 44% of vote 4 (well done) and 43% of vote 5 (very well done), correlated with the saved time (p<0.001). The relative volume differences and similarity indexes suggested a better inter-agreement of manually adjusted DL-based VOIs than manually segmented ones.ConclusionsThe application of the DL-tool resulted satisfactory, especially in complex delineation cases, improving the ROs inter-agreement of delineated VOIs and saving time.