Potential of preoperative fluorodeoxyglucose-positron emission tomography/computed tomography to diagnose contralateral lymph node metastases in patients with oral cancer.

OBJECTIVE: To establish a decision tree using preoperative PET/computed tomography (CT) parameters for detecting contralateral lymph node metastasis (CLNM) in oral cancer patients. METHODS: In total, 140 patients with a confirmed histopathological diagnosis of oral carcinoma showed fluorodeoxyglucose accumulation of contralateral lymph nodes in PET images. Of 260 lymph nodes, eight were metastatic. We compared metastatic and non-metastatic nodes using Fisher-Freeman-Halton exact and Fisher's exact and Mann-Whitney U tests. The diagnostic performance was analyzed using receiver operating characteristic curves. We established a decision tree using exhaustive chi-squared automatic interaction detection algorithm. RESULTS: Five PET/CT parameters were significantly different between metastatic and non-metastatic nodes: the longest ( P  = 0.015) and shortest ( P  = 0.023) diameter, and the maximum standardized uptake values (SUVmax) of the contralateral node ( P  = 0.030), primary tumor ( P  < 0.001), and ipsilateral node ( P  < 0.001). The area under the curves of SUVmax of the primary tumor (0.887), ipsilateral node (0.886), and longest diameter (0.752) were the largest and these three parameters were used as predictive criteria in the decision tree. The accuracy of the decision tree was 97.7% with 100% sensitivity and 97.6% specificity. CONCLUSION: Preoperative PET/CT imaging with a decision tree has the potential to diagnose CLNM in patients with oral cancer.

The magnetic resonance imaging characteristics of radicular cysts and granulomas.

OBJECTIVES: Limited studies have differentiated radicular cysts and granulomas with MRI. Therefore, we investigated the MRI characteristics of the two lesions and clarified features for distinguishing between them. METHODS: We collected data of 27 radicular cysts and 9 granulomas definitively diagnosed by histopathology and reviewed the fat-saturated T2 weighted, T1 weighted, and contrast-enhanced fat-saturated T1 weighted images. We measured the maximum diameter and apparent diffusion coefficient values of the lesions. We employed Fisher's exact test, the Mann-Whitney U test, and independent t-tests to compare the two lesions and created a decision tree for discriminating between them. RESULTS: There were significant differences between radicular cysts and granulomas with respect to five imaging characteristics-signal intensity of the lesion centre on fat-saturated T2 weighted images; signal intensity, texture, and contrast enhancement of the lesion centre on contrast-enhanced fat-saturated T1 weighted images; and maximum diameter of the lesion. The cut-off diameter for radicular cysts was 15.9 mm. The area under the receiver operating characteristic curve, sensitivity, and specificity were 0.971, 85.2%, and 100%, respectively. CONCLUSIONS: From the decision tree analysis, maximum diameter, lesion centre contrast enhancement on contrast-enhanced fat-saturated T1 weighted images, and lesion centre signal intensity on fat-saturated T2 weighted images were important for discriminating between radicular cysts and granulomas.

Differentiation of cystic lesions in the jaw by conventional magnetic resonance imaging and diffusion-weighted imaging.

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.