Joseph Stancanello

About

Joseph Stancanello is a leading figure in the integration of advanced imaging, radiomics, and artificial intelligence into radiation oncology. His research focuses on leveraging machine learning and deep learning to enhance treatment planning and predict tumor response, particularly in the context of stereotactic body radiation therapy (SBRT) for non-small cell lung cancer. In a highly cited 2021 study (34 citations), Stancanello demonstrated that combining pre-treatment computed tomography (CT) with biologically effective dose features significantly improves the accuracy of predictive models, marking a major contribution to personalized radiotherapy. He has also explored comparative treatment modalities, notably arguing for the superiority of high-intensity focused ultrasound over radiation therapy for early-stage prostate cancer. Additionally, his work on the evolution of arteriovenous malformation radiosurgery—from LINAC-based techniques to image-guided robotic systems—and his forward-looking perspective on the obsolescence of traditional L-shaped gantry linear accelerators underscore his influence on technological innovation in the field. Stancanello’s interdisciplinary approach continues to shape the future of precision oncology.

Research Focus

Key Achievements

Top Papers

1. 1
   Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

   34 citations · 2021
2. 2
   High intensity focused ultrasound may be superior to radiation therapy for the treatment of early stage prostate cancer

   5 citations · 2011
3. 3
   Arteriovenous Malformation Radiosurgery: Evolution of the Technique

   3 citations · 2006
4. 4
   The traditional L‐shaped gantry for radiotherapy linear accelerators will soon become obsolete

   2 citations · 2010

Key Collaborators

Contact & Links