MRI STAGING ACCURACY AND DETERMINANTS OF STAGING DISCREPANCY IN NASOPHARYNGEAL CARCINOMA: A MULTICENTRE COMPARATIVE IMAGING STUDY ACROSS CENTRAL AND PROVINCIAL HOSPITALS IN VIETNAM

Phạm Thị Bích Đào1,2, Trần Văn Tâm3, Nguyễn Diệu My4, Nguyễn Minh Đức5, Tống Xuân Thắng4, Phạm Trần Anh 4, Nguyễn Thi Anh Đào6, Nguyễn Thị Quỳnh Trang6, Phan Xuân Nam7, Hoàng Hữu Trường8, Nguyễn Thi Ngọc Anh9, Phạm Thị Phương Hạnh10
1 Hanoi Medical University;
2 Hanoi Medical University Hospital;
3 Hanoi Medical University Hospital
4 Hanoi Medical University
5 Deparment of medical service administration - Ministry of health Viet Nam
6 University of Medicine and Pharmacy, Vietnam National University Hanoi
7 Quang Tri General Hospital
8 Thanh Hoa General Hospital
9 University of Medicine and Pharmacy, Thai Nguyen University
10 Ninh Binh Department of Health

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Tóm tắt

Objective: To compare the accuracy of magnetic resonance imaging (MRI) staging for nasopharyngeal carcinoma (NPC) between central and provincial hospitals in Vietnam; to quantify agreement between institutional reports and expert consensus; and to identify institutional, technical, and workflow factors associated with staging discrepancy.

Methods: This multicentre retrospective comparative imaging study included 536 MRI examinations from 536 patients with histologically confirmed NPC treated or evaluated at five Vietnamese hospitals (three central and two provincial). Institutional MRI reports were compared with a blinded consensus re-reading by two senior head-and-neck radiologists. Six staging criteria were assessed: parapharyngeal extension, skull-base or bony invasion, retropharyngeal lymph-node involvement, suspicious subcentimetric cervical lymph nodes, intracranial extension, and cavernous sinus involvement. Agreement was quantified with Cohen’s kappa. Case-level logistic regression was used to identify predictors of any staging discrepancy. Criterion-level associations were explored separately to avoid unit-of-analysis error.

Results: Central hospitals showed higher point estimates of sensitivity, specificity, and positive predictive value across all six MRI criteria. The largest inter-tier gaps were observed for suspicious subcentimetric cervical lymph nodes (sensitivity 79.6% vs. 51.3%) and retropharyngeal lymph-node detection (83.4% vs. 57.9%). Agreement between institutional reports and expert consensus was excellent or substantial at central hospitals (overall κ=0.83) but only moderate at provincial hospitals (overall κ=0.52). Overall staging discrepancy affected 23.7% of central cases and 75.2% of provincial cases (p<0.001). A treatment-plan change was recommended after expert review in 5.6% of central cases and 29.8% of provincial cases. In the case-level model, provincial hospital tier (adjusted odds ratio [aOR] 3.14), absence of double reading (aOR 2.47), use of 1.5T MRI (aOR 2.03), absence of regular multidisciplinary team meetings (aOR 1.94), and absence of PACS/RIS (aOR 1.72) were the strongest predictors of discrepancy.

Conclusions: MRI staging discrepancy in NPC was substantially more frequent at provincial hospitals, particularly for nodal criteria that require high spatial resolution and subspecialty experience. Double reading, structured reporting, MDT review, PACS/RIS connectivity, and staged MRI infrastructure upgrades are the most actionable priorities. These findings identify the staging criteria most suitable for AI-assisted second-reader tools designed to reduce diagnostic inequality across hospital tiers.

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Tài liệu tham khảo

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AUTHOR DATA-CHECK NOTES BEFORE SUBMISSION
• Confirm that the imaging cohort is exactly 536 MRI examinations from 536 patients, and that central/provincial denominators are 375 and 161.
• Add TP/FP/FN/TN and Wilson 95% confidence intervals to Table 2 when raw criterion-level matrices are available.
• Run the final case-level model with hospital-level clustering or mixed effects if possible, because several predictors are institutional variables.
• Run the final criterion-level model using GEE or mixed-effects logistic regression on the 536 × 6 criterion dataset.
• Verify that ethics approval language accurately reflects whether all data were retrospective and de-identified before analysis.
• Remove this data-check section before journal submission.