Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy

Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy

Background and Purpose:: A comprehensive understanding of radiotherapy toxicity requires analysis of multimodal data. However, it is challenging to develop a model that can analyse both 3D imaging and clinical data simultaneously. In this study, a deep learning model is proposed for simultaneously a...

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Detaylı Bibliyografya
Asıl Yazarlar: Behnaz Elhaminia, Alexandra Gilbert, Andrew Scarsbrook, John Lilley, Ane Appelt, Ali Gooya
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: Elsevier 2025-01-01
Seri Bilgileri:Physics and Imaging in Radiation Oncology
Konular:
Radiotherapy toxicity
Deep learning
Toxicity prediction
Convolutional network
Attention
Pelvic radiotherapy
Online Erişim:http://www.sciencedirect.com/science/article/pii/S2405631625000156

Internet

http://www.sciencedirect.com/science/article/pii/S2405631625000156

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