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Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN)

Justin Visak¹, Enobong Inam¹, Boyu Meng¹

¹Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Journal of Applied Clinical Medical Physics

|March 6, 2023

Summary

This summary is machine-generated.

Machine learning-guided plans offer superior quality for head & neck adaptive radiotherapy (ART) compared to traditional methods. This AI-guided approach enhances plan optimization within the Varian Ethos system, improving outcomes.

Keywords:

adaptive radiotherapy head & neck machine-learning

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Area of Science:

Radiation Oncology
Medical Physics
Artificial Intelligence in Healthcare

Background:

Varian Ethos employs an intelligent optimization engine (IOE) for automated treatment planning.
The IOE's black-box nature presents challenges for improving plan quality.
Evaluating machine learning (ML) for initial reference plan generation in head & neck (H&N) adaptive radiotherapy (ART) is crucial.

Purpose of the Study:

To assess ML-guided initial reference plan generation for H&N ART.
To compare AI-guided, knowledge-based planning (KBP-RTOG), and RTOG-only approaches for IOE input.
To evaluate IOE sensitivity to different clinical input goals.

Main Methods:

Retrospective re-planning of 20 H&N cancer patients using Varian Ethos.
Utilized a fixed 18-beam intensity-modulated radiotherapy (IMRT) template.
Generated clinical goals via an in-house deep-learning dose predictor (AI-Guided), a commercial KBP model (KBP-RTOG), and an RTOG constraint template (RTOG).
Assessed target coverage, organs-at-risk (OAR) doses, and plan deliverability against clinical benchmark plans.

Main Results:

AI-guided plans demonstrated superior quality compared to KBP-RTOG and RTOG-only plans.
OAR doses were comparable or improved with AI-guided plans versus benchmarks.
KBP-RTOG and RTOG plans showed increased OAR doses but generally met RTOG criteria.
All plans achieved a Heterogeneity Index <1.07.

Conclusions:

AI-guided planning yields the highest quality for H&N ART.
KBP-enabled and RTOG-only plans are feasible for ART adoption.
IOE performance is sensitive to input goals; align inputs with institutional planning directives.