First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma

Abstract

The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC). This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process. From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min. We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.