Nataliya Kovalchuk, Clinical Professor at Stanford Radiation Oncology, shared a post on LinkedIn:
“Excited to share our latest manuscript published in Frontiers in Oncology: ‘Clinical implementation of an automated VMAT treatment planning script for head and neck cancer patients: three-year experience’
This work represents more than just developing an automated planning script, it demonstrates the successful clinical implementation and sustained use of automation over 3 years for head and neck (HN) radiotherapy at Stanford Radiation Oncology .
We evaluated 1,000 consecutive HN cancer patients (500 manual plans before implementation and 500 automated plans after implementation), making this one of the largest real-world clinical evaluations of automated treatment planning.
Our key findings:
- Automated plans maintained excellent target coverage and dose homogeneity.
- Significant reductions in dose to multiple critical organs at risk, including the brainstem, spinal cord, parotid glands, submandibular glands, cochleae, oral cavity, larynx, pharynx, esophagus, mandible, and lips.
- In a blinded physician review, 94% of automated plans were rated clinically acceptable (vs. 86% of manual plans), with most physicians preferring the automated plans.
- After three years of routine clinical use, the improvements in organ-at-risk sparing remained consistent across 500 consecutive automated patients, demonstrating that automation can improve both quality and consistency in clinical practice.
Automation is not about replacing expertise, it is about allowing clinicians and physicists to consistently deliver high-quality treatment plans while improving efficiency and reducing variability.
Lots of gratitude to the project lead Yong Yang and all the co-authors for their countless hours of work that made this project possible.”
Title: Clinical implementation of an automated VMAT treatment planning script for head and neck cancer patients: three-year experience
Authors: Nataliya Kovalchuk, Peng Dong, Caressa Hui, Ignacio Romero, Ziyi Wang, Lina Shah, Raveena Pandya, Michael Xiang, Everett Moding, Michael Gensheimer, Beth Beadle, Quynh-Thu Le, Lei Xing, Yong Yang
Read the Full Article.
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