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Accurate and efficient dose calculation remains a major challenge in boron neutron capture therapy (BNCT) due to the complexity of neutron transport and the contribution of secondary particles. Conventional Monte Carlo (MC) methods are often too computational...

Proton therapy for breast cancer presents unique dosimetric challenges due to frequent use of range shifters and potential proton penetration into lungs. The study aims to compare dosimetric results between a novel proton treatment planning system (TPS) with...

To validate the accuracy of CuraProton, a newly developed proton therapy treatment planning system, through comprehensive type testing of depth dose distributions and transverse dose profiles across multiple clinically complex scenarios, in strict compliance...

The tumor-to-normal ratio of maximum uptake (Tmax/N) on 18F-BPA PET is widely used to guide decision-making regarding boron neutron capture therapy (BNCT). However, intratumoral heterogeneity of boron drug distribution may also influence clinical outcomes. Th...

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...

[To investigate the clinical accuracy and feasibility of markerless optical-guided self-positioning for breast cancer patients undergoing radiotherapy.]

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...

This study proposes an automated quality assurance (QA) method for radiation therapy structure delineation based on the RT contour QA software, addressing issues such as low efficiency in delineating clinical radiation therapy regions of interest (ROIs), sign...

Radiation dermatitis (RD) is a primary acute toxicity in proton therapy for H&N cancer patients. Since skin typically resides in the distal fall-off region of the posterior or posterior oblique beams, the radiobiological dose may increase and lead to more sev...

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...

Although the number of proton centers continues to rise worldwide, rapid methods for commissioning and dose verification are still lacking. To evaluate the performance of a GPU-accelerated Monte Carlo (MC) dose computational framework for patient-specific pla...

This study proposed the residual dynamic transformer-nnU-Net (RDT-nnU-Net) for automated delineation of esophageal cancer and adjacent organs at risk (OARs) in radiotherapy, aiming to improve accuracy, coverage, and adaptability across radiotherapy strategies.

To evaluate a deep-learning based real-time automated radiotherapy planning pipeline for nasopharyngeal carcinoma (NPC) in both retrospective multi-center study and prospective clinical deployment.

Target delineation remains one of the most significant workflow bottlenecks in radiotherapy. Particularly, CTV segmentation remains challenging because it depends on clinical reasoning beyond visible imaging features. This study aims to develop and evaluate a...

Ultrahigh dose-rate (FLASH) radiotherapy delivers radiation within millisecond timescales, yet direct measurement of radiolytic oxidative processes during beam delivery remains technically challenging. This study aimed to develop and apply a real-time optical...