Dosimetry Validation Study for Automated Head and Neck Cancer Organs at Risk Segmentation Using Stratified Learning and Neural Architecture Search

Abstract

<h3>Purpose/Objective(s)</h3> Organs at risk (OARs) segmentation is an essential process in head and neck (H&N) cancer radiotherapy. We have reported high automated segmentation geometric accuracy of Stratified Learning and Neural Architecture Search method in terms of Dice Score (DSC) from our previous study. In this study, we would evaluate the dosimetric influence of our automated approach before integrating this method into the clinical workflow. <h3>Materials/Methods</h3> To measure the dosimetric effects brought by the OARs' variance, the intensity-modulated radiotherapy (IMRT) dose plans of 10 head and neck cancer patients were replanned using the original tumor target volumes and three substitute OAR contours permutations (deep learning generated stratified organs at risk segmentation (SOARS), SOARS revised by physician (SOARS-revised), and OAR delineated from scratch by physician (human reader)). We further examined the clinical dosimetric accuracy and the clinical reference OAR contours were overlaid on top of each replanned dose grid to evaluate the dosimetric differences. <h3>Results</h3> After replanning, SOARS and SOARS-revised contours have slightly smaller Diff (max dose) as compared to human reader contours (3.4%, 3.5% vs. 4.1%). For the Diff (mean dose), human reader, SOARS, SOARS-revised achieves similar results, i.e., 5.3%, 5.0%, and 5.0%, respectively. However, more OARs from the human reader have dose variations larger than 10% or 20% as compared to SOARS and SOARS-revised. Overall, our results indicate that using OAR contours from human reader, SOARS, and SOARS-revised lead to generally comparable dose accuracy in clinical practice. SOAR-related OAR contours have fewer OARs with dose error larger than 10% or 20%. <h3>Conclusion</h3> This study further validates the clinical applicability of a deep learning based automated H&N OAR segmentation method by comparing dosimetry of plans using OAR contours generated automatically and by a human reader to the gold standard contours. The dose variations calculated after planning on automated segmentation contours are less than 5%. Our proposed automated H&N OAR segmentation method not only achieves high geometric accuracy but also helps deliver treatment beams with little variances.