Application of diffusion kurtosis imaging to odontogenic lesions: Analysis of the cystic component

Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts

We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 × 10-6 mm2/s, OKC: 925 × 10-6 mm2/s, R: 2718 × 10-6 mm2/s, and M: 2686 × 10-6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts.

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.

Application of Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Differentiating Benign and Malignant Head and Neck Lesions

BACKGROUND

Preoperative differentiation of head and neck lesions is important for treatment plan selection.

PURPOSE

To evaluate the diagnostic value of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating benign from malignant head and neck lesions and subgroups, including lymphoma subgroup (LS), Warthin's tumor subgroup (WS), malignant tumor subgroup (excluding lymphoma) (MTS), and benign tumor subgroup (excluding Warthin's tumor) (BTS).

STUDY TYPE

Retrospective.

POPULATION

Seventy-four patients with 79 head and neck lesions (44 benign, 35 malignant), divided into four subgroups: LS (14), WS (12), MTS (21), and BTS (32).

FIELD STRENGTH/SEQUENCES

A 3.0 T, single-shot echo-planar sequence with 5 b-values for DKI and enhanced T1 high-resolution isotropic volume excitation (eTHRIVE) sequence for DCE-MRI.

ASSESSMENT

The mean diffusivity (MD) and mean kurtosis (MK) derived from DKI and the time-signal intensity curve (TIC), peak time (T_peak ), and washout ratio (WR) based on DCE-MRI were measured. The diagnostic efficiencies of DKI and DCE-MRI, alone and in combination, were calculated and compared. The parameters mentioned above were compared between the four subgroups.

STATISTICAL TEST

Mann-Whitney U test, chi-square test, receiver operating characteristic curve, Delong test, one-way analysis of variance test, and Kruskal-Wallis H test. A P value < 0.05 was considered statistically significant.

RESULTS

The combination of TIC and parameters of DKI and DCE-MRI for differentiating benign and malignant lesions with 94.94% accuracy is superior to DKI or DCE-MRI alone with approximately 75% accuracy. MD, MK, T_peak , and WR showed significant differences among the four subgroups. The accuracy of MD and MK was 91.14% and 92.41% for differentiating BTS from the other three subgroups. WR achieved 100% accuracy for discriminating WS from LS or MTS. MD and MK both differentiated LS from MTS with 97.14% accuracy.

DATA CONCLUSION

A combination of DKI and DCE-MRI can effectively differentiate head and neck lesions with good accuracy.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Uterine Cervical Carcinoma: Evaluation Using Non-Gaussian Diffusion Kurtosis Imaging and Its Correlation With Histopathological Findings

OBJECTIVE

The aim of the study was to assess non-Gaussian diffusion kurtosis imaging (DKI)'s usefulness as a noninvasive method to evaluate tumor invasion depth, histological grade, and lymph node metastasis in cervical carcinoma (CC) patients.

METHODS

Twenty-two consecutive patients with histologically confirmed CC were examined by 1.5-T MRI and non-Gaussian DKI with 4 b values of 0, 500, 1000, and 2000 s/mm2. Kurtosis (K), diffusivity (D), and apparent diffusion coefficient (ADC) maps were compared with histopathological findings.

RESULTS

Kurtosis maps revealed the fibrous stroma as a distinct high K zone (1.442 ± 0.373) that was significantly different from values of the cervical mucosa, outer stroma, and parametrium (0.648 ± 0.083, 0.715 ± 0.113, and 0.504 ± 0.060, respectively, P < 0.0001). Kurtosis (1.189 ± 0.228) and D (0.961 ± 0.198 × 10-3 mm2/s) values of all CCs were significantly different from those of all uterine cervical wall layers. Kurtosis and D values were significantly correlated with histological grades of CCs (r = 0.934, P < 0.0001, and r = -0.925, P < 0.0001, respectively), whereas no significant differences were found in ADC values between grades 2 and 3 CCs (P = 0.787). Metastatic and nonmetastatic lymph nodes showed significantly different K (P < 0.0001) and D (P < 0.0001) values; however, their ADC values did not show significant differences (P = 0.437). For differentiating grade 3 CCs from grade 1 or 2 CCs, the areas under the curve for K (0.991, P = 0.0375) and D (0.982, P = 0.0337) values were significantly higher than those for ADC values (0.759). For differentiating metastatic and nonmetastatic lymph nodes, the areas under the curve for K (0.974, P = 0.0028) and D (0.968, P = 0.0018) values were significantly higher than those for ADC (0.596).

CONCLUSIONS

Non-Gaussian DKI may be clinically useful for noninvasive evaluation of tumor invasion depth, histological grade, and lymph node metastasis in CC patients.

Imaging of Radiolucent Jaw Lesions

Radiolucent lesions in the jaw bones comprise a whole spectrum of odontogenic and nonodontogenic lesions. Although the imaging appearance is not always specific, careful radiologic analysis may contribute to characterization of these lesions. A useful approach is to first analyze the absence or presence of a relationship of the lesion to the teeth. The relation may be either near the tooth apex or crown of the tooth. Other lesions may or may not show any specific anatomical location. After analysis of the primary location of the lesion, additional criteria that may help in further imaging characterization are lesion demarcation and morphology, involvement of the cortex and periosteum, and soft tissue changes. This article describes the most characteristic and prevalent radiolucent lesions of the jaws at each location. In routine clinical practice, cone beam computed tomography is sufficient for appropriate lesion characterization, although magnetic resonance imaging may be useful in selected cases.

Odontogenic keratocyst: imaging features of a benign lesion with an aggressive behaviour

Abstract

The latest (4th) edition of the World Health Organization (WHO) Classification of Head and Neck Tumours, published in January 2017, has reclassified keratocystic odontogenic tumour as odontogenic keratocyst. Therefore, odontogenic keratocysts (OKCs) are now considered benign cysts of odontogenic origin that account for about 10% of all odontogenic cysts. OKCs arise from the dental lamina and are characterised by a cystic space containing desquamated keratin with a uniform lining of parakeratinised squamous epithelium. The reported age distribution of OKCs is considerably wide, with a peak of incidence in the third decade of life and a slight male predominance. OKCs originate in tooth-bearing regions and the mandible is more often affected than the maxilla. In the mandible, the most common location is the posterior sextant, the angle or the ramus. Conversely, the anterior sextant and the third molar region are the most common sites of origin in the maxilla. OKCs are characterised by an aggressive behaviour with a relatively high recurrence rate, particularly when OKCs are associated with syndromes. Multiple OKCs are typically associated with the nevoid basal cell carcinoma syndrome (NBCCS), an autosomal dominant multisystemic disease. Radiological imaging, mainly computed tomography (CT) and, in selected cases, magnetic resonance imaging (MRI), plays an important role in the diagnosis and management of OKCs. Therefore, the main purpose of this pictorial review is to present the imaging appearance of OKCs underlining the specific findings of different imaging modalities and to provide key radiologic features helping the differential diagnoses from other cystic and neoplastic lesions of odontogenic origin.

Key Points

• Panoramic radiography is helpful in the preliminary assessment of OKCs.

• CT is considered the tool of choice in the evaluation of OKCs.

• MRI with DWI or DKI can help differentiate OKCs from other odontogenic lesions.

• Ameloblastoma, dentigerous and radicular cysts should be considered in the differential diagnosis.

• The presence of multiple OKCs is one of the major criteria for the diagnosis of NBCCS.

MR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient

PURPOSE

To perform a meta-analysis for assessing the accuracy of diffusion kurtosis imaging (DKI)-derived quantitative parameters (kurtosis values, K; and corrected diffusion coefficients non-Gaussian bias, D) in separating malignant cancers from benign lesions.

METHODS

Relevant studies were searched in PubMed and Cochrane Library databases and were analyzed by Meta-DiSc software.

RESULTS

Fourteen eligible studies involving 1847 lesions in 1107 patients (895 were benign and 952 were malignant) were included. Pooled analysis showed the sensitivity, specificity, positive likelihood ratio (LR), and negative LR were respectively 0.83 (95% CI, 0.79-0.85), 0.83 (95% CI, 0.80-0.86), 4.61 (95% CI, 2.98-7.14), and 0.22 (95% CI, 0.18-0.28) for K, with the overall area under curve (AUC) of 0.89. The sensitivity, specificity, positive LR, and negative LR were 0.85 (95% CI, 0.80-0.88), 0.85 (95% CI, 0.79-0.89), 6.39 (95% CI, 3.14-12.99), and 0.18 (95% CI, 0.14-0.23) for D, with the overall AUC of 0.92. The sensitivity, specificity, positive LR, and negative LR for apparent diffusion coefficient (ADC) derived from standard diffusion-weighted imaging (DWI) were 0.82 (95% CI, 0.79-0.84), 0.85 (95% CI, 0.82-0.88), 4.75 (95% CI, 3.38-6.68), and 0.24 (95% CI, 0.19-0.29), with the overall AUC of 0.89. The superiority of D to K and ADC was also confirmed by the subgroup analysis of prostate cancer.

CONCLUSION

Our findings suggest that DKI should be added to the routine imaging protocol for screening cancer, with the highest diagnostic accuracy of diffusion coefficients.

Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from Benign Masses in Head and Neck Region

Objective

To compare the diagnostic performance of readout-segmented echo-planar imaging (RS-EPI)-based diffusion kurtosis imaging (DKI) and that of diffusion-weighted imaging (DWI) for differentiating malignant from benign masses in head and neck region.

Materials and Methods

Between December 2014 and April 2016, we retrospectively enrolled 72 consecutive patients with head and neck masses who had undergone RS-EPI-based DKI scan (b value of 0, 500, 1000, and 1500 s/mm²) for pretreatment evaluation. Imaging data were post-processed by using monoexponential and diffusion kurtosis (DK) model for quantitation of apparent diffusion coefficient (ADC), apparent diffusion for Gaussian distribution (D_app), and apparent kurtosis coefficient (K_app). Unpaired t test and Mann-Whitney U test were used to compare differences of quantitative parameters between malignant and benign groups. Receiver operating characteristic curve analyses were performed to determine and compare the diagnostic ability of quantitative parameters in predicting malignancy.

Results

Malignant group demonstrated significantly lower ADC (0.754 ± 0.167 vs. 1.222 ± 0.420, p < 0.001) and D_app (1.029 ± 0.226 vs. 1.640 ± 0.445, p < 0.001) while higher K_app (1.344 ± 0.309 vs. 0.715 ± 0.249, p < 0.001) than benign group. Using a combination of D_app and K_app as diagnostic index, significantly better differentiating performance was achieved than using ADC alone (area under curve: 0.956 vs. 0.876, p = 0.042).

Conclusion

Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance. Its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.

State of the art MRI in head and neck cancer

Head and neck cancer affects more than 11,000 new patients per year in the UK¹ and imaging has an important role in the diagnosis, treatment planning, and assessment, and post-treatment surveillance of these patients. The anatomical detail produced by magnetic resonance imaging (MRI) is ideally suited to staging and follow-up of primary tumours and cervical nodal metastases in the head and neck; however, anatomical images have limitations in cancer imaging and so increasingly functional-based MRI techniques, which provide molecular, metabolic, and physiological information, are being incorporated into MRI protocols. This article reviews the state of the art of these functional MRI techniques with emphasis on those that are most relevant to the current management of patients with head and neck cancer.