Peking University Cancer Hospital & Institute

To develop cross-attention-based multi-modal deep learning models and to preliminarily validate their performance for predicting local recurrence (LR) in patients with non-small cell lung cancer (NSCLC).

Accurate delineation of the esophageal clinical target volume (CTV) is challenging due to extended longitudinal coverage and substantial slice-wise shape variation. Existing deep learning methods typically require extensive manual revision. Interactive segmen...

Dual-energy CT (DECT) enables material differentiation by exploiting the energy-dependent attenuation characteristics of tissues, which is particularly beneficial for carbon ion therapy. This study systematically evaluated a recently proposed machine-learning...

To propose a deep-learning approach for predicting high from low-energy 4D-CBCT.

To develop risk-stratification and prognosis-prediction models for limited-stage small cell lung cancer (LS-SCLC) patients, suggesting potential candidates that may benefit from the new treatment protocol using high-dose hyperfractionated simultaneous integra...

To investigate the potential impact of dose rate, beam energy, and temporal structure on the FLASH effect, an irradiation platform providing widely tunable dose rate and energy range is desirable. This work aims to develop and characterize a superconducting a...

To propose and validate a novel dosimetric method integrating inter-fractional temporal dose changes for improved SCLC prognosis management and individualized decision-making.

Head-and-neck cancer (HNC) treatment planning is challenging due to the close proximity of multiple critical organs-at-risk (OARs) to complex target volumes. Intensity-modulated carbon-ion therapy (IMCT) is attractive for HNC due to superior dose conformity a...

The escalating global burden of cancer necessitates reliable and broadly generalizable automated treatment planning (ATP) systems. Current data-driven ATP approaches often exhibit insufficient generalization when applied to diverse clinical prescriptions. To...