The authors Phong Dai Lam and Ami Kuribayashi equally contributed to this work.Objective: To determine the optimal diagnostic criterion of dynamic contrast-enhanced MRI (DCE-MRI) for predicting salivary gland malignancy using a dynamic sequence with high temporal resolution, as well as the accuracy of this technique. Methods: The DCE-MRI findings of 98 salivary gland tumours (74 benign and 24 malignant) were reviewed. MR images were sequentially obtained at 5-s intervals for 370 s. Two parameters, peak time and washout ratio (WR) were determined from the time-signal intensity curve. The optimal thresholds of these parameters for differentiating benign and malignant tumours were determined, along with the diagnostic accuracy of the criterion using these thresholds. Results: A peak time of 150 s and a WR of 30% were identified as optimal thresholds. As the criterion for malignancy, the combination of peak time ,150 s and WR ,30% provided a sensitivity of 79% (19/24), specificity of 95% (70/74) and an overall accuracy of 91% (89/98). Three of the five false-negative cases were malignant lymphomas of the parotid gland. Conclusion: Peak time ,150 s with WR ,30% comprised the optimal diagnostic criterion in predicting salivary gland malignancy, providing a sensitivity of 79% and specificity of 95%. The use of high temporal resolution might improve the accuracy of DCE-MRI. Advances in knowledge: Although several studies have reported the usefulness of DCE-MRI in the differential diagnosis of salivary gland tumours, the specific diagnostic criteria employed have differed widely. We determined the optimal criterion and its accuracy using a dynamic sequence with high temporal resolution.
Objectives: This study aimed to determine the discrimination power of apparent diffusion coefficient (ADC) for cystic lesions in the jaw using MRI. Methods: We selected 127 cystic lesions, comprising dentigerous cysts (DCs), odontogenic keratocysts (OKCs), and unicystic ameloblastomas (UABs), from our MRI database examined by 3T MRI, including diffusion-weighted imaging sequences, and we reviewed their imaging characteristics. We attempted to discriminate the three types of lesions by ADC values with receiver operator characteristic analysis; however, satisfactory results were not obtained for differentiation between DC and OKC. Therefore, we performed a decision tree analysis. Results: The imaging characteristics of the lesions were significantly different according to Fisher’s exact test, except for differences in sex. The ADC values statistically discriminated the lesions of DC and UAB, OKC and UAB, but not DC and OKC. Thus, differentiation was performed by a decision tree for DC and OKC by evaluating the following points: the attached tooth condition, signal intensity on the T1 weighted image (T1SI), ADC value, and the cyst site. However, cases showing hypo- or isointense T1SI with an ADC value under 1.168 × 10–3 mm2/s were difficult to differentiate. Conclusion: The ADC value helped distinguish UAB from both DC and OKC, but not DC from OKC. However, the decision tree based on ADC value, tooth contact status, and T1SI helped differentiate DC and OKC to some extent.
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