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Rank #109 · 9 unique linked submissions.
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Uncertainty-aware multimodal biomarker-guided treatment response prediction in metastatic NSCLC remains a critical unmet need to support robust therapy selection and adaptation over time. We developed a multimodal framework integrating FDG-PET, T-cell recepto...
Organ‑level breast dosimetry is essential for developing robust dose-response models of subsequent breast cancer (SBC) risk in female childhood cancer survivors. Historically, epidemiologic studies relied on surrogate metrics such as prescription dose due to...
Lung cancer is a leading global malignancy with high mortality. Radiotherapy is a critical treatment; however, current planning often suffers from subjective dose settings and side effects. This study aims to use a Conditional Generative Adversarial Network (...
Linear accelerator (LINAC) coordinate system conventions have historically evolved heterogeneously, with vendors and institutions adopting mixed non-IEC standards. While clinically functional, this lack of uniformity introduces added complexity and potential...
VMAT total body irradiation (TBI) planning is complex and time-intensive, involving multiple isocenters, junction management, and iterative hotspot control. Many automation approaches still require repeated user interaction, limiting efficiency gains. This wo...
Multiscale treatment response prediction in advanced NSCLC enables spatially informed dose painting, yet prediction point estimates alone do not convey the uncertainty required for adaptive therapy decision support. We developed a multiscale conformal predict...
Accurate assessment of early radiotherapy response in tumors provides crucial guidance for optimizing radiotherapy protocols. We developed a 3D deep learning model termed Attention Med3D based on transfer learning and attention mechanisms for predicting mid-t...
Accurate identification of high-risk and low-risk tumor subregions enables radiographers to customize radiation dose distributions for biologically adaptive therapies. This study proposes a 3DUNET-GMM model that integrates 3D-UNet feature extracting with Gaus...
Accurate prediction of tumor response during chemoradiotherapy is essential for treatment optimization but remains challenging. We developed a deep learning model based on a Dual Path Network (DPN), which is a hybrid architecture combining elements of ResNet...