An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction
B Lou, S Doken, T Zhuang, D Wingerter… - The Lancet Digital …, 2019 - thelancet.com
Background Radiotherapy continues to be delivered without consideration of individual
tumour characteristics. To advance towards more precise treatments in radiotherapy, we …
tumour characteristics. To advance towards more precise treatments in radiotherapy, we …
Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study
Background Artificial intelligence (AI) and deep learning have shown great potential in
streamlining clinical tasks. However, most studies remain confined to in silico validation in …
streamlining clinical tasks. However, most studies remain confined to in silico validation in …
Handcrafted versus deep learning radiomics for prediction of cancer therapy response
In The Lancet Digital Health, Bin Lou and colleagues1 apply deep learning methods to
analyse pre-treatment CT scans in a retrospective cohort study of 944 patients (849 in the …
analyse pre-treatment CT scans in a retrospective cohort study of 944 patients (849 in the …
Deep learning for radiotherapy outcome prediction using dose data–a review
Artificial intelligence, and in particular deep learning using convolutional neural networks,
has been used extensively for image classification and segmentation, including on medical …
has been used extensively for image classification and segmentation, including on medical …
Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review
B Rusanov, GM Hassan, M Reynolds, M Sabet… - Medical …, 2022 - Wiley Online Library
The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained
popularity as computational resources and algorithmic sophistication have advanced in …
popularity as computational resources and algorithmic sophistication have advanced in …
Radiomics and deep learning in lung cancer
Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …
learning. By providing a three-dimensional characterization of the lesion, models based on …
Deep learning: a review for the radiation oncologist
Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural
networks to create a model. The application areas of deep learning in radiation oncology …
networks to create a model. The application areas of deep learning in radiation oncology …
Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study
Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …
courses and outcomes, even within the same tumor stage. This study explores deep …
Deep learning predicts lung cancer treatment response from serial medical imaging
Purpose: Tumors are continuously evolving biological systems, and medical imaging is
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …
Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural …
Purpose Patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy
(IMRT) is a ubiquitous clinical procedure, but conventional methods have often been …
(IMRT) is a ubiquitous clinical procedure, but conventional methods have often been …