Author profile
Icahn School of Medicine at Mount Sinai
To systematically assess whether commonly proposed architectural enhancements provide measurable benefits for deep learning-based radiotherapy dose prediction, using controlled comparisons of 3D U-Net variants to support evidence-based model selection and est...
Objective assessment of radiotherapy plans is challenging because expert assessment relies on complex, multidimensional tradeoffs that are not fully captured by predefined dose-volume constraints. This study aims to quantitatively interpret expert treatment p...
Accurate prediction of radiation-induced toxicity is crucial for optimizing radiotherapy outcomes, yet most existing models rely on supervised learning with clinician-graded toxicity scores that are susceptible to patient self-reporting errors and intra-obser...
Radiation pneumonitis (RP) remains a clinically significant dose-limiting toxicity in thoracic radiotherapy. Accurate RP prediction is challenging due to its multifactorial etiology and complex interactions among contributing factors. Although multimodal data...
Accurate prediction of radiation-induced toxicity is crucial for optimizing radiotherapy outcomes. However, most existing predictive models rely on uni-modal data and deterministic models that are vulnerable to label noise and uncertainty. This study aims to...
Accurate real-time tumor tracking is critical for MRI-guided radiotherapy, where geometric uncertainty can significantly increase dose to surrounding critical organs. Continuous cine-MRI enables motion-adaptive treatment. However, accurate tracking under larg...
Consistently automating clinically acceptable plans without human intervention remains a challenge in radiotherapy. While knowledge-based planning (KBP) predicts optimal achievable dose-volume metrics, it often fails to achieve these metrics without manual ad...
Knowledge-based planning (KBP) improves plan quality and efficiency. However, training institution-specific models requires substantial clinical data and expertise, and publicly available models may not align with local clinical objectives. This study evaluat...
To evaluate whether a Large Language Model (LLM)–driven autonomous planning system can self-learn planning strategies from human planner logs and apply this knowledge to generate clinically compatible radiotherapy plans without manual refinements.
Existing deep learning-based dose prediction methods primarily learn empirical mappings between anatomy and dose, without modeling beam delivery physics. This gap may limit their robustness and accuracy, especially in heterogeneous regions where dose depositi...
Monitoring real-time pancreatic target motion during radiotherapy is challenging. The diaphragm can be tracked by ultrasound; however, published findings on its correlation with abdominal motion are inconsistent. We aimed to develop a robust algorithm to asse...