The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Background: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.

Methods: Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice.

German radiation oncology's next generation: a web-based survey of young biologists, medical physicists, and physicians-from problems to solutions.

Background: Radiation science is of utmost significance not only due to its growing importance for clinical use, but also in everyday life such as in radiation protection questions. The expected increase in cancer incidence due to an aging population combined with technical advancements further implicates this importance and results in a higher need for sufficient highly educated and motivated personnel. Thus, factors preventing young scientists and medical personnel from entering or remaining in the field need to be identified.

Assessing the practice of total neoadjuvant therapy for rectal cancer: an online survey among radiation oncology departments in Germany and German-speaking regions of Austria and Switzerland.

Total neoadjuvant therapy (TNT) of rectal cancer improves rates of pathological complete remission and progression-free survival. With improved clinical response rates, interest grew in a non-operative approach/watch and wait (WaW) for this disease. In 2020, the working groups of ACO/AIO/ARO published a consensus statement on the use of TNT, including a non-operative approach.

Effects of Climate, Soil, Topography and Disturbance on Liana Prevalence.

Lianas (woody vines and climbing monocots) are increasing in abundance in many tropical forests with uncertain consequences for forest functioning and recovery following disturbances. At a global scale, these increases are likely driven by disturbances and climate change. Yet, our understanding of the environmental variables that drive liana prevalence at regional scales is incomplete and geographically biased towards Latin America.