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Issue: Vol. 71 No. 77 (2026)
Section: Articles
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Pham, T. B. D., Pham, H. T., Khong, V. D., Tran, V. T., Le, M. D., Bui, T. M., Nguyen, D. M., Nguyen, T. H., Tong, X. T., Pham, T. A., Pham, T. B. T., Nguyen, T. A. D., Nguyen, X. H., Nguyen, T. Q. T., Phan, X. N., Hoang, H. T., Dinh, T. A., Tran, H. Y., Nguyen, T. N. A., … Bui, Q. T. DEVELOPMENT OF A DIAGNOSTIC READINESS INDEX FOR NASOPHARYNGEAL CARCINOMA AT OTORHINOLARYNGOLOGY FACILITIES. Vietnam Journal of Otorhinolaryngology - Head and Neck Surgery, 71(77). Retrieved September 2, 2026, from https://tapchitaimuihong.vn/index.php/tmh/article/view/411

DEVELOPMENT OF A DIAGNOSTIC READINESS INDEX FOR NASOPHARYNGEAL CARCINOMA AT OTORHINOLARYNGOLOGY FACILITIES

Thi Bich Dao Pham1, , Huy Tan Pham2, Van Dong Khong3, Van Tam Tran2, Minh Dat Le2, Thi Mai Bui2, Dieu My Nguyen1, Thi Hang Nguyen2, Xuan Thang Tong1, Tran Anh Pham1, Thi Bich Thuy Pham4, Thi Anh Dao Nguyen2, Xuan Hoa Nguyen2, Thị Quynh Trang Nguyen5, Xuan Nam Phan6, Huu Truong Hoang7, Tuan Anh Dinh4, Hai Yen Tran1, Thi Ngoc Anh Nguyen8, Van Dang Nguyen1, Thi Thanh Huong Nguyen9, Thanh Thuy Nguyen10, Quang Tuyen Bui11
1 Trường Đại học Y Hà Nội
2 Bệnh viện đại học Y Hà Nội
3 Tổng công ty Giải pháp doanh nghiệp Viettel, tập đoàn công nghiệp viễn thông quân đội
4 Bệnh viện Tai Mũi Họng Trung ương
5 Trường Đại học Y Dược, Đại học Quốc Gia Hà NộiTrường Đại học Y Dược, Đại học Quốc Gia Hà Nội
6 Bệnh viện tỉnh Quảng Trị
7 Bệnh viện đa khoa tỉnh Thanh Hóa
8 Trường đại học Y dược, Đại học Thái Nguyên
9 Viện khoa học đo đạc và bản đồ
10 Viện dữ liệu không gian
11 Học viện Viettel, tập đoàn công nghiệp viễn thông quân đội

Main Article Content

Abstract

This cross-sectional descriptive study combined with index development used survey data from five hospitals and clinical data from 2,118 patients with nasopharyngeal carcinoma at provincial-level hospitals. The proposed diagnostic readiness index included five components: advanced endoscopy, routine nasopharyngeal MRI, PACS/image storage, double reading, and multidisciplinary team consultation. Provincial-level hospitals had lower rates of advanced endoscopy, routine MRI, double reading, and multidisciplinary team consultation than central-level hospitals, at 40.1% versus 74.5%, 20.0% versus 74.3%, 20.0% versus 51.4%, and 20.0% versus 60.0%, respectively. Patient data showed that 89.5% presented late, 63.9% were diagnosed at stage III–IV, and 51.6% had unilateral tinnitus. This index helps quantify early detection capacity and identify bottlenecks in equipment, digital infrastructure, double reading, and multidisciplinary coordination at local otorhinolaryngology facilities.

Keywords

nasopharyngeal carcinoma, diagnostic readiness index, otorhinolaryngology, early detection, MRI

Article Details

References

[1] WHO/IARC. Global Cancer Observatory, GLOBOCAN 2022. Lyon: International Agency for Research on Cancer; 2022.
[2] Chang ET, Adami HO. The enigmatic epidemiology of nasopharyngeal carcinoma. Cancer Epidemiol Biomarkers Prev. 2006;15(10):1765-1777. doi:10.1158/1055-9965.EPI-06-0353.
[3] Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Primers. 2020;6(1):92. doi:10.1038/s41572-020-00224-3.
[4] Wong LM, King AD, Ai QYH, et al. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI. Eur Radiol. 2021;31(6):3856-3863. doi:10.1007/s00330-020-07451-y.
[5] Chen H, Qi Y, Yin Y, et al. MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma. arXiv. 2018;1812.10033.
[6] Zeng Y, Zeng P, Shen S, et al. DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning. Front Oncol. 2023;13:1190075. doi:10.3389/fonc.2023.1190075.
[7] Tang P, et al. Dense SegU-net for automatic head-and-neck tumor segmentation in MR images. arXiv. 2020;2006.06278.
[8] Yue Y, Zeng X, Lin H, et al. A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images. NPJ Digit Med. 2024;7(1):384. doi:10.1038/s41746-024-01403-2.
[9] He Z, Zhang K, Zhao N, et al. Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy. iScience. 2023;26(10):107463. doi:10.1016/j.isci.2023.107463.
[10] Li S, Wan X, Deng YQ, et al. Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued. Cancer Imaging. 2023;23(1):14. doi:10.1186/s40644-023-00530-5.
[11] Gu B, Meng M, Bi L, et al. Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics. Front Oncol. 2022;12:899351. doi:10.3389/fonc.2022.899351.
[12] Meng M, Gu B, Bi L, Song S, Feng DD, Kim J. DeepMTS: Deep Multi-Task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT. IEEE J Biomed Health Inform. 2022;26(9):4497-4507. doi:10.1109/JBHI.2022.3181791.
[13] Li S, Deng YQ, Hua HL, et al. Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI. Comput Methods Programs Biomed. 2022;219:106785. doi:10.1016/j.cmpb.2022.106785.
[14] Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep. 2023;13(1):8984. doi:10.1038/s41598-023-35795-0.
[15] Zhu J, Zhang S, Yu R, et al. An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images. Quant Imaging Med Surg. 2021;11(4):1368-1380. doi:10.21037/qims-20-538.