Stereotactic body radiotherapy (SBRT) for spinal metastases requires accurate delineation of the spinal cord and thecal sac to maximize treatment efficacy while minimizing toxicity. Deep learning-based segmentation models improve contouring efficiency and red...
Author profile
Tomohiro Kajikawa
Department of Radiology, Kyoto Prefectural University of Medicine
Deep Learning-Based Auto-Segmentation of Spinal Cord and Thecal Sac In Stereotactic Body Radiotherapy for Spinal Metastases
Poster Program · Therapy Physics
Predicting Achievable Cardiac Dose Reduction with Deep Inspiration Breath-Hold Using Free-Breathing CT and Deep Learning
Deep-inspiration breath-hold radiotherapy (DIBH) is widely used in left-sided breast cancer radiotherapy to reduce heart dose, but the dosimetric benefit varies among patients. Assessing DIBH eligibility typically requires additional CT simulation and treatme...
Poster Program · Therapy Physics
Radiomics- and Dosiomics-Based Machine Learning Models for Predicting 1- and 2-Year Local Failure after SBRT for Non-Spinal Bone Metastases
Stereotactic body radiotherapy (SBRT) for non-spinal bone metastases generally achieves high local control; however, approximately 10% of patients experience local failure (LF). Conventional clinical and dose metrics often fail to capture patterns associated...
Poster Program · Therapy Physics