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MRI Posttreatment Surveillance for Head and Neck Squamous Cell Carcinoma: Proposed MR NI-RADS Criteria
M M Ashour1, E A F Darwish2, R M Fahiem2
1From the Department of Diagnostic Radiology, Faculty of Medicine, Ain Shams University, Cairo, Egypt manar.ashour@gmail.com.
Background And Purpose:
The current Neck Imaging Reporting and Data System (NI-RADS) criteria were designed for contrast-enhanced CT with or without PET. Prior studies have revealed the capability of DWI and T2 signal intensity in distinguishing locoregional tumor residual and recurrence from posttreatment benign findings in head and neck cancers. We aimed to propose MR imaging NI-RADS criteria by adding diffusion criteria and T2 signal intensity to the American College of Radiology NI-RADS template.
Materials And Methods:
This retrospective study included 69 patients with head and neck squamous cell carcinoma (HNSCC) who underwent posttreatment contrast-enhanced MRI imaging surveillance using a 1.5T scanner. The scans were interpreted by 2 neuroradiologists. Image analysis assessed the primary tumor site using the current American College of Radiology NI-RADS morphologic lexicon (mainly designed for contrast-enhanced CT with or without PET). NI-RADS rescoring was then performed based on our proposed criteria using T2 signal and diffusion features. The reference standard was a defined set of criteria, including clinical and imaging follow-up and pathologic assessment.
Results:
Imaging assessment of treated HNSCC at the primary tumor site using T2 signal intensity and diffusion features as modifying rules to NI-RADS showed higher sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (92.3%, 90.7%, 85.7%, 95.1%, and 91.3%, respectively) compared with the current NI-RADS lexicon alone (84.6%, 81.4%, 73.3%, 89.8%, and 82.6%, respectively).
Conclusions:
The addition of diffusion features and T2 signal to the American College of Radiology NI-RADS criteria for the primary tumor site enhances the specificity, sensitivity, positive predictive value, negative predictive value, and NI-RADS accuracy.
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