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...

To develop and validate RAISE (Radiotherapy Accelerated by Intelligent Spatially-Enhanced Segmentation) across multiple tumor sites and centers.

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.

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 an explainable deep learning framework for histological segmentation and prognostic modeling of neuroendocrine tumors (NET) liver metastasis, comparing the efficacy of non-linear deep survival analysis against traditional linear Cox regression.

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 advance sparse-information constrained respiratory modeling toward an information-augmentation driven paradigm by incorporating data augmentation, multimodality guidance, prior-informed representation, and improved optimization pipelines.

To propose a multimodal surrogates-guided, multi-task respiratory-modeling framework for simultaneous anatomical motion reconstruction and tumor-tracking.

Accurate assessment of locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) is critical for treatment stratification. However, manual Tumor Regression Grade (TRG) suffers from inter-observer variability and subjective bias. Thi...

Predicting pathological complete response (pCR) following neoadjuvant immunotherapy (nICT) is critical for personalized management of esophageal cancer. This study develops an interpretable multimodal framework that integrates pre-treatment 3D CT latent featu...

To evaluate the prognostic value of deep learning–derived spatial uncertainty in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

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...