James M. Lamb

Artificial intelligence holds significant promise for radiation oncology, yet clinical adoption is frequently stalled by the gap between conceptual understanding and practical implementation. This three-part educational track provides a start-to-finish playbo...

Aggregating large radiotherapy datasets for predictive modeling is impeded by inconsistent nomenclature. To address this, we developed a fully automated, metadata-independent framework to classify treatment plans into six Anatomic Regions and identify specifi...

Large, well-annotated databases are necessary for the development of personalized outcomes models. We evaluated the viability of using a Large Language Model (LLM) to extract patient-specific specific toxicity and progression outcomes from unstructured radiol...

To evaluate dosimetric trends and robustness of online adaptive radiation therapy for focal-boosted prostate SBRT. Materials/

Deep learning–based dose prediction models have demonstrated high accuracy in multi-institutional challenges; however, their performance under real-world institutional domain shift remains unclear. This study evaluates the generalizability of a state-of-the-a...

Accurate delineation of Head and Neck (H&N) Gross Tumor Volumes (GTV) is a prerequisite for effective radiotherapy, represents a significant cognitive challenge, and may contribute to an observed outcomes deficit between high-volume and low-volume H&N radioth...

Online Adaptive Radiotherapy (oART) is resource and time-intensive, requiring dosimetrist, physicist, and physician involvement at each adapted fraction. The therapist-led adaptive workflow has been shown to reduce physician console-time. This study evaluates...

The incorrect patient placed on the radiotherapy couch for treatment is rare, but the consequences are potentially devastating. We developed an artificial intelligence assistance tool intended to support therapists at the treatment console to ensure the corre...

In investigating image-based lung ventilation, a fundamental question must be answered: is the measurement technique repeatable? Repeatability of ventilation imaging in PET and CT have been studied, but MRI is emerging. Crucial to deriving ventilation is the...

Large-scale radiotherapy research increasingly relies on heterogeneous DICOM datasets containing complex cross-references across imaging and radiotherapy objects. Beyond ingestion, downstream preprocessing for analysis and machine learning requires consistent...

Recent advances in MRI could offer patients with benign lung disease whole-lung ventilation mapping that may serve as a surrogate for pathophysiological changes such as increased airway resistance and decreased lung compliance. Ventilation maps can be derived...