Author profile
Fudan University Shanghai Cancer Center
Accurate radiotherapy dose prediction largely depends on beam configuration, but most deep learning-based dose prediction models rely on explicit beam Angle input, which is not feasible in the early stages of planning. This study proposes a unified framework...
Breast cancer is the second leading cause of cancer death among women. Fortunately, for this biologically heterogeneous disease, several molecular subtypes corresponding to distinct responses to treatments and prognoses have been recognized, enabling subtype-...
To develop a clinically oriented framework for patient-specific 3D dose prediction in head-and-neck radiotherapy, emphasizing DVH-consistent performance for target coverage and OAR sparing.
Neoadjuvant chemoradiotherapy (nCRT) improves local control in patients with locally advanced rectal cancer (LARC); however, substantial heterogeneity exists in postoperative recurrence risk. This study aimed to evaluate the feasibility of predicting postoper...
To investigate the feasibility and robustness of using a VQ-VAE based deep learning model for predicting daily anatomical changes in rectal cancer radiotherapy using a large-scale dataset.
Adaptive radiotherapy (ART) requires accurate and efficient delineation of the planning target volume (PTV) on daily imaging. However, conventional automatic segmentation methods rely on large-scale, high-quality annotations, while registration-based contour...
To develop and evaluate a structure-guided multi-task deep learning framework for synthesizing positron emission tomography (PET) images from computed tomography (CT) in patients with esophageal cancer, aiming to improve both global image fidelity and tumor-s...