AI Article Synopsis
- - The study evaluates the effectiveness of a deep learning-based AI model for automatically delineating clinical target volume (CTV) and organs at risk (OARs) in lung cancer patients receiving postoperative radiotherapy (PORT), addressing the challenges of manual delineation.
- - A comparison was made among three contouring techniques: unmodified AI auto-segmentation, fully manual delineation by junior residents, and AI-assisted delineation, with the latter proving to be the most accurate and efficient according to various metrics.
- - Results showed that AI-assisted delineation significantly outperformed both unmodified AI and manual methods in terms of accuracy, leading to improved consistency in OAR delineation and overall contouring effectiveness.
Article Abstract
Background: Postoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process.
Objective: To evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions.
Methods: We previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV).
Results: AI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P<0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency.
Conclusion: AI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579590 | PMC |
| http://dx.doi.org/10.3389/fonc.2024.1388297 | DOI Listing |
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