Preliminary study of using diffusion kurtosis imaging for characterizing parotid gland tumors

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study

PURPOSE

To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors.

METHOD

From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index.

RESULTS

For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05).

CONCLUSIONS

Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Current status of diffusion-weighted imaging in differentiating parotid tumors

Recently, diffusion-weighted imaging (DWI) is an essential magnetic resonance imaging (MRI) protocol for head and neck imaging in clinical practice as it plays an important role in lesion detection, tumor extension evaluation, differential diagnosis, therapeutic effect prediction, therapy evaluation, and recurrence diagnosis. Especially in the parotid gland, several studies have already attempted to achieve accurate differentiation between benign and malignant tumors using DWI. A conventional single-shot echo-planar-based DWI is widely used for head and neck imaging, whereas advanced DWI sequences, such as intravoxel incoherent motion, diffusion kurtosis imaging, periodically rotated overlapping parallel lines with enhanced reconstruction, and readout-segmented echo-planar imaging (readout segmentation of long variable echo-trains), have been used to characterize parotid tumors. The mean apparent diffusion coefficient values are easily measured and useful for assessing cellularity and histological characteristics, whereas advanced image analyses, such as histogram analysis, texture analysis, and machine and deep learning, have been rapidly developed. Furthermore, a combination of DWI and other MRI protocols has reportedly improved the diagnostic accuracy of parotid tumors. This review article summarizes the current state of DWI in differentiating parotid tumors.

Advanced magnetic resonance imaging findings in salivary gland tumors

Salivary gland tumors (SGTs) make up a small portion (approximately 5%) of all head and neck tumors. Most of them are located in the parotid glands, while they are less frequently located in the submandibular glands, minor salivary glands or sublingual gland. The incidence of malignant or benign tumors (BTs) in the salivary glands varies according to the salivary gland from which they originate. While most of those detected in the parotid gland tend to be benign, the incidence of malignancy increases in other glands. The use of magnetic resonance imaging (MRI) in the diagnosis of SGTs is increasing every day. While conventional sequences provide sufficient data on the presence, localization, extent and number of the tumor, they are insufficient for tumor specification. With the widespread use of advanced techniques such as diffusion-weighted imaging, semi-quantitative and quantitative perfusion MRI, studies and data have been published on the differentiation of malignant or BTs and the specificity of their subtypes. With diffusion MRI, differentiation can be made by utilizing the cellularity and microstructural properties of tumors. For example, SGTs such as high cellular Warthin’s tumor (WT) or lymphoma on diffusion MRI have been reported to have significantly lower apparent diffusion values than other tumors. Contrast agent uptake and wash-out levels of tumors can be detected with semi-quantitative perfusion MRI. For example, it is reported that almost all of the pleomorphic adenomas show an increasing enhancement time intensity curve and do not wash-out. On quantitative perfusion MRI studies using perfusion parameters such as Ktrans, Kep, and Ve, it is reported that WTs can show higher Kep and lower Ve values than other tumors. In this study, the contribution of advanced MRI to the diagnosis and differential diagnosis of SGTs will be reviewed.

Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the differentiation of parotid gland tumors

OBJECTIVE

To assess the usefulness of combined diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of parotid gland tumors.

METHODS

Seventy patients with 80 parotid gland tumors who underwent DKI and DCE-MRI were retrospectively enrolled and divided into four groups: pleomorphic adenomas (PAs), Warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). DCE-MRI and DKI quantitative parameters were measured. The Kruskal-Wallis H test and post hoc test with Bonferroni correction and ROC curve were used for statistical analysis.

RESULTS

WTs demonstrated the highest K_ep value (median 1.89, interquartile range [1.46-2.31] min^-1) but lowest V_e value (0.20, [0.15-0.25]) compared with PAs (K_ep, 0.34 [0.21-0.55] min^-1; V_e, 0.36 [0.24-0.43]), OBTs (K_ep, 1.22 [0.27-1.67] min^-1; V_e, 0.28 [0.25-0.41]), and MTs (K_ep, 0.71 [0.50-1.23] min^-1; V_e, 0.35 [0.26-0.45]) (all p < .05). MTs had the lower D value (1.10, [0.88-1.29] × 10^-3 mm²/s) compared with PAs (1.81, [1.60-2.20] × 10^-3 mm²/s) and OBTs (1.57, [1.32-1.89] × 10^-3 mm²/s) (both p < .05). PAs had the lower K^trans value (0.12, [0.07-0.18] min^-1) compared with OBTs (0.28, [0.11-0.50] min^-1) (p < .05). The cutoff values of combined K_ep and V_e, D, and K^trans to distinguish WTs, MTs, and PAs sequentially were 1.06 min^-1, 0.28, 1.46 × 10^-3 mm²/s, and 0.21 min^-1, respectively (accuracy, 89% [71/80], 91% [73/80], 78% [62/80], respectively).

CONCLUSION

The combined use of DKI and DCE-MRI may help differentiate parotid gland tumors.

KEY POINTS

• The combined use of DKI and DCE-MRI could facilitate the understanding of the pathophysiological characteristics of parotid gland tumors. • A stepwise diagnostic diagram based on the combined use of DCE-MRI parameters and the diffusion coefficient is helpful for accurate preoperative diagnosis in parotid gland tumors and may further facilitate the clinical management of patients.

Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?

OBJECTIVE

To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach.

METHODS

Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists' diagnosis, and radiomic models were evaluated.

RESULTS

Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T₂ weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma.

CONCLUSION

MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist.

ADVANCES IN KNOWLEDGE

Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T₂ weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

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.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

RESULTS

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

CONCLUSION

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

Performance of diffusion-weighted imaging for the diagnosis of parotid gland malignancies: A meta-analysis

PURPOSE

This study aimed to assess the diagnostic performance of diffusion-weighted imaging (DWI) for parotid gland malignancies.

METHODS

Four databases (PubMed, the Cochrane Library, Embase, and Web of Science) were searched systematically and retrospectively by two researchers until May 18, 2020. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to pool the sensitivity and specificity data for the apparent diffusion coefficient (ADC). Summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. The positive (LR+) and negative likelihood ratios (LR-) were also calculated. Subgroup and meta-regression analyses were performed to evaluate heterogeneity within studies.

RESULTS

Sixteen studies involving 1004 patients were included. The pooled sensitivity, specificity, and AUC for the ADC to distinguish malignant from begin parotid lesions were 89 %, 76 %, and 0.91, respectively. The LR + was 3.7 and LR- was 0.15, respectively. Subgroup analyses revealed that the applied cut-off b values and study size were sources of heterogeneity for the ADC. There were publication bias concerns.

CONCLUSIONS

Our meta-analysis suggests that the ADC value provides excellent sensitivity and moderate specificity for the diagnosis of malignant lesions in the parotid gland. However, substantial heterogeneity was found. Therefore, additional larger, prospective studies in combination with standard techniques focusing on parotid tumors should be conducted to determine the true performance of DWI for the differential diagnosis of parotid lesions.

Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model

OBJECTIVE

To evaluate the feasibility of using imaging parameters (D, β and μ) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors.

METHODS

15 b-value (0-2000 s/mm²) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity) and a microstructural quantity μ of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin's tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis.

RESULTS

None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; β, p = 0.967; μ, p = 0.693). WT showed significantly lower D (p < 0.001) and β (p < 0.001), while higher μ (p = 0.001) than PA. Combination of D, β and μ showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and β (p = 0.025) than PA, while no significant difference was found on μ (p = 0.064). Combination of D and β showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on β (p = 0.027) between MT and WT, while not on D (p = 0.806) and μ (p = 0.789). Setting a βof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806).

CONCLUSION

A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors.

ADVANCES IN KNOWLEDGE

(1) PA showed higher D and β and lower μ than WT. (2) PA had higher D and β than MT. (3) WT demonstrated lower β than MT. (4) β, as a new FROC parameter, could offer an added value to the differentiation.

Diffusion MR Imaging in the Head and Neck: Principles and Applications

Diffusion imaging is a functional MR imaging tool that creates tissue contrast representative of the random, microscopic translational motion of water molecules within human body tissues. Long considered a cornerstone MR imaging sequence for brain imaging, diffusion-weighted imaging (DWI) increasingly is used for head and neck imaging. This review reports the current state of diffusion techniques for head and neck imaging, including conventional DWI, DWI trace with apparent diffusion coefficient map, diffusion tensor imaging, intravoxel incoherent motion, and diffusion kurtosis imaging. This article describes background physics, reports supportive evidence and potential pitfalls, highlights technical advances, and details practical clinical applications.

Added value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland tumors

PURPOSE

To assess the added value of susceptibility-weighted imaging (SWI) to diffusion-weighted imaging (DWI) in the characterization of parotid gland tumors.

METHODS

Seventy-eight patients with pathologically confirmed parotid gland tumors, who underwent DWI and SWI for pre-surgery evaluation, were enrolled. Apparent diffusion coefficient (ADC) and degree of intratumoral susceptibility signal intensity (ITSS) were measured and compared between benign and malignant groups, and among pleomorphic adenoma (PA), Warthin tumor (WT) and malignant tumor (MT). Independent sample t test, one-way analysis of variance and receiver operating characteristic curve analysis were used for statistical analyses.

RESULTS

Benign parotid gland tumor showed a significantly higher mean ADC value than malignant tumors (0.836 ± 0.350 vs 0.592 ± 0.163, p = 0.001). Setting an average ADC value of 0.679 as the cut-off value, optimal differentiating performance could be obtained (AUC, 0.700; sensitivity, 62.69%; specificity, 81.82%) for differentiating malignant from benign tumors. PA showed significantly higher mean ADC and less ITSS than WT (ADC, p < 0.001; ITSS, p = 0.033) and MT (ADC, p < 0.001; ITSS, p = 0.024), while the difference between WT and MT was not significant (ADC, p = 0.826; ITSS, p = 0.539). After integration with ITSS, the diagnostic performance of ADC was improved for differentiating PA from WT (AUC 0.921 vs 0.873) and from MT (AUC 0.906 vs 0.882).

CONCLUSION

SWI could provide added information to DWI and serve as a supplementary imaging marker for the characterization of parotid gland tumors.

Multiparametric Magnetic Resonance Imaging for the Diagnosis and Differential Diagnosis of Parotid Gland Tumors

The majority of salivary gland tumors occur in the parotid glands. Characterization (ie, benign or malignant, and histological type), location (deep or superficial), and invasion into the neighboring tissues of parotid tumors determine preoperative treatment planning. MRI gives more information than other imaging methods about the internal structure, localization, and relationship with other tissues of parotid tumors. Functional MRI methods (diffusion-weighted imaging, dynamic contrast-enhanced MRI, perfusion-weighted MRI, MR spectroscopy, etc.) have been increasingly used recently to increase the power of radiologists to characterize the tumors. Although they increase the workload of radiologists, the combined use of functional MRI methods improves accuracy in the differentiation of the tumors. There are a wide range of studies in the literature dealing with the combined use of different functional imaging methods in combination with conventional sequences. The aim of the present review is to evaluate conventional and functional/advanced MR methods, as well as multiparametric MRI applications combining them in the diagnosis of parotid gland tumors. Evidence Level: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:11-32.