Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma
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AbstractBackground
Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck radiation therapy is labor intensive and highly variable among radiation oncologists.

Purpose
To construct and validate an artificial intelligence (AI) contouring tool to automate primary gross tumor volume (GTV) contouring in patients with NPC.

Materials and Methods
In this retrospective study, MRI data sets covering the nasopharynx from 1021 patients (median age, 47 years; 751 male, 270 female) with NPC between September 2016 and September 2017 were collected and divided into training, validation, and testing cohorts of 715, 103, and 203 patients, respectively. GTV contours were delineated for 1021 patients and were defined by consensus of two experts. A three-dimensional convolutional neural network was applied to 818 training and validation MRI data sets to construct the AI tool, which was tested in 203 independent MRI data sets. Next, the AI tool was compared against eight qualified radiation oncologists in a multicenter evaluation by using a random sample of 20 test MRI examinations. The Wilcoxon matched-pairs signed rank test was used to compare the difference of Dice similarity coefficient (DSC) of pre- versus post-AI assistance.

Results
The AI-generated contours demonstrated a high level of accuracy when compared with ground truth contours at testing in 203 patients (DSC, 0.79; 2.0-mm difference in average surface distance). In multicenter evaluation, AI assistance improved contouring accuracy (five of eight oncologists had a higher median DSC after AI assistance; average median DSC, 0.74 vs 0.78; P < .001), reduced intra- and interobserver variation (by 36.4% and 54.5%, respectively), and reduced contouring time (by 39.4%).

Conclusion
The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival.
Acceptance Date26/03/2019
All Author(s) ListLi Lin, Qi Dou, Yue-Ming Jin, Guan-Qun Zhou, Yi-Qiang Tang, Wei-Lin Chen, Bao-An Su, Feng Liu, Chang-Juan Tao, Ning Jiang, Jun-Yun Li, Ling-Long Tang, Chuan-Miao Xie, Shao-Min Huang, Jun Ma, Pheng-Ann Heng, Joseph T. S. Wee, Melvin L. K. Chua, Hao Chen, Ying Sun
Journal nameRadiology
Year2019
Month6
Volume Number291
Issue Number3
Pages677 - 686
ISSN0033-8419
LanguagesEnglish-United States

Last updated on 2020-31-05 at 02:25