English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Radiation therapy is the primary method used to treat cancer in the clinic.Its goal is to deliver a precise dose to the planning target volume (PTV) whileprotecting the surrounding organs at risk (OARs). However, the traditionalworkflow used by dosimetrists to plan the treatment is time-consuming andsubjective, requiring iterative adjustments based on their experience. Deeplearning methods can be used to predict dose distribution maps to address theselimitations. The study proposes a cascade model for organs at risk segmentationand dose distribution prediction. An encoder-decoder network has been developedfor the segmentation task, in which the encoder consists of transformer blocks,and the decoder uses multi-scale convolutional blocks. Another cascadeencoder-decoder network has been proposed for dose distribution predictionusing a pyramid architecture. The proposed model has been evaluated using anin-house head and neck cancer dataset of 96 patients and OpenKBP, a public headand neck cancer dataset of 340 patients. The segmentation subnet achieved 0.79and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed theexisting baselines. The dose distribution prediction subnet outperformed thewinner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVHscores, respectively. The predicted dose maps showed good coincidence withground truth, with a superiority after linking with the auxiliary segmentationtask. The proposed model outperformed state-of-the-art methods, especially inregions with low prescribed doses.

A Cascade Transformer-based Model for 3D Dose Distribution Prediction in Head and Neck Cancer Radiotherapy