Adam Dicker, Chief Medical Officer of OncoHost and Senior Vice President, Enterprise Radiation Oncology at Thomas Jefferson University Hospitals, shared a post on LinkedIn about a paper Peter Ciaccio co-authored with colleagues:
“I’m thrilled to share groundbreaking research from our team at Thomas Jefferson – we’ve developed and validated an AI-powered system that’s transforming how we plan radiation therapy for lung cancer patients.
THE CHALLENGE:
Accurately contouring lung lobes is critical for predicting pulmonary toxicity in radiation therapy, but it’s extremely time-consuming and complex. This has limited our ability to perform lobe-specific dosimetry analysis in routine clinical practice.
THE INNOVATION:
Our multi-institutional team developed an AI auto-contouring tool using deep learning (residual 3D U-Net) that automatically segments all five lung lobes on standard treatment planning CT scans.
THE RESULTS:
- 93% overall accuracy (Dice Similarity Coefficient: 0.93)
- Validated across 50 patients from multiple institutions
- Works with free-breathing CT scans (standard in radiation oncology)
- High accuracy across all five lung lobes
WHY IT MATTERS:
- Recent studies show lower lung lobe dose correlates with radiation pneumonitis risk
- With ~256,000 new respiratory cancer cases annually in the US, this technology can impact thousands of patients
- Enables functional sub-unit dosimetry analysis without adding burden to clinical workflows
- Reduces planning time while improving treatment precision
This work represents a significant step forward in personalized radiation therapy. By automating lung lobe segmentation, we can now routinely evaluate dose distribution at the lobar level – helping us better predict and mitigate treatment toxicity.
Huge congratulations to our collaborators at Atrium Health Wake Forest Baptist, Cooper Health System, MIM Software Inc., and Montefiore Health System!
Published in Reports of Practical Oncology and Radiotherapy.”
Title: Development and validation of an AI-based lung lobe auto-contouring tool using radiation therapy planning free-breathing images
Authors: Peter Ciaccio, Joseph Lombardo, Andrew Fuquay, Soroush Pahlavian, Rachel Grimm, Jun Kang, Wookjin Choi, Paul Sullivan, Yevgeniy Vinogradskiy
