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Rank #96 · 10 unique linked submissions.
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This study investigates whether explicitly learning anatomical information in the image domain, when jointly optimized with dose prediction, can improve the accuracy and robustness of CBCT-based dose synthesis.
To develop and validate an attention-based deep learning (DL) model to predict overall survival (OS) in patient with brain metastases (BM) using pre-treatment magnetic resonance image (MRI).
The purpose of this study is to utilize clinical pathological information and preoperative ultrasound images of breast cancer to predict the risk of brain metastasis by combining radiomics and deep learning.
Esophageal cancer is a lethal malignancy where early detection significantly improves survival. While opportunistic screening using chest CT is promising, the frequent absence of Contrast-Enhanced CT (CECT) in routine exams limits diagnostic accuracy, as Non-...
CBCT is essential for daily anatomy assessment in ART, but artifacts and unstable HUs impair dose accuracy, and existing corrections or sCT add uncertainties. This study introduces a unified framework for direct CBCT dose correction and DIR-based auto-segment...
Survival prediction based on radiomics and deep learning may aid treatment decision-making and follow-up management. This study aims to develop and validate an integrated model integrating multimodal imaging (MRI and CT), radiomics, and deep learning features...
Accurate, frequent bladder volume monitoring is essential in pelvic radiotherapy because inter-fraction filling variability affects target coverage and organ-at-risk sparing. Yet current options are suboptimal: CBCT offers 3D anatomy but increases radiation a...
Cervical cancer (CC) remains one of the most common malignancies in women worldwide, and postoperative recurrence continues to challenge long-term survival. Given that clinical decision-making relies on multimodal information, integrating imaging, textual, an...
To introduce a hybrid quantum-classical machine learning (QML) approach and validate its feasibility and accuracy for pretreatment radiation esophagitis (RE) prediction in patients with esophageal cancer (EC) undergoing radiotherapy and/or chemotherapy.
Accurate medical image segmentation is crucial for clinical workflows such as radiotherapy planning and longitudinal disease monitoring. However, high-quality pixel/voxel annotations are costly and time-consuming to obtain, limiting large-scale supervised tra...