Objective: Recent in vivo results have shown prominent tissue sparing effect of radiotherapy with ultra-high dose rates (FLASH) compared to conventional dose rates (CONV). Oxygen depletion has been proposed as the underlying mechanism, but in vitro data to support this have been lacking. The aim of the current study was to compare FLASH to CONV irradiation under different oxygen concentrations in vitro. Methods: Prostate cancer cells were irradiated at different oxygen concentrations (relative partial pressure ranging between 1.6 and 20%) with a 10 MeV electron beam at a dose rate of either 600 Gy/s (FLASH) or 14 Gy/min (CONV), using a modified clinical linear accelerator. We evaluated the surviving fraction of cells using clonogenic assays after irradiation with doses ranging from 0 to 25 Gy. Results: Under normoxic conditions, no differences between FLASH and CONV irradiation were found. For hypoxic cells (1.6%), the radiation response was similar up to a dose of about 5–10 Gy, above which increased survival was shown for FLASH compared to CONV irradiation. The increased survival was shown to be significant at 18 Gy, and the effect was shown to depend on oxygen concentration. Conclusion: The in vitro FLASH effect depends on oxygen concentration. Further studies to characterize and optimize the use of FLASH in order to widen the therapeutic window are indicated. Advances in knowledge: This paper shows in vitro evidence for the role of oxygen concentration underlying the difference between FLASH and CONV irradiation.

No abstract

No abstract

Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures.Annotation standards exist,but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures. Delineated prostate organs at risk (OAR), support-and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D Inception-ResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1-3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic. A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data. Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use.