Multiparametric MR imaging of sinonasal diseases: time-signal intensity curve- and apparent diffusion coefficient-based differentiation between benign and malignant lesions

Dynamic contrast enhanced MRI of the head and neck region using a VIBE sequence with Cartesian undersampling and compressed sensing

OBJECTIVES

Compressed sensing allows for image reconstruction from sparsely sampled k-space data, which is particularly useful in dynamic contrast enhanced MRI (DCE-MRI). The aim of the study was to assess the diagnostic value of a volume-interpolated 3D T1-weighted spoiled gradient-echo sequence with variable density Cartesian undersampling and compressed sensing (CS) for head and neck MRI.

METHODS

Seventy-one patients with clinical indications for head and neck MRI were included in this study. DCE-MRI was performed at 3 Tesla magnet using CS-VIBE (variable density undersampling, temporal resolution 3.4 s, slice thickness 1 mm). Image quality was compared to standard Cartesian VIBE. Three experienced readers independently evaluated image quality and lesion conspicuity on a 5-point Likert scale and determined the DCE-derived time intensity curve (TIC) types.

RESULTS

CS-VIBE demonstrated higher image quality scores compared to standard VIBE with respect to overall image quality (4.3 ± 0.6 vs. 4.2 ± 0.7, p = 0.682), vessel contour (4.6 ± 0.4 vs. 4.4 ± 0.6, p < 0.001), muscle contour (4.4 ± 0.5 vs. 4.5 ± 0.6, p = 0.302), lesion conspicuity (4.5 ± 0.7 vs. 4.3 ± 0.9, p = 0.024) and showed improved fat saturation (4.8 ± 0.3 vs. 3.8 ± 0.4, p < 0.001) and movement artifacts were significantly reduced (4.6 ± 0.6 vs. 3.7 ± 0.7, p < 0.001). Standard VIBE outperformed CS-VIBE in the delineation of pharyngeal mucosa (4.2 ± 0.5 vs. 4.6 ± 0.6, p < 0.001). Lesion size in cases where a focal lesion was identified was similar for all readers for CS-VIBE and standard VIBE (p = 0.101). TIC curve assessment showed good interobserver agreement (k=0.717).

CONCLUSION

CS-VIBE with variable density Cartesian undersampling allows for DCE-MRI of the head and neck region with diagnostic, high image quality and high temporal resolution.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions

OBJECTIVES

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.

MATERIALS AND METHODS

From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes.

RESULTS

The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970).

CONCLUSION

In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity.

CLINICAL RELEVANCE STATEMENT

Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions.

KEY POINTS

• Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Differentiation of lacrimal gland tumors using the multi-model MRI: classification and regression tree (CART)-based analysis

BACKGROUND

Little is known about the value of dynamic contrast-enhanced (DCE) in combination with diffusion-weighted imaging (DWI) for the differentiation of lacrimal gland tumors.

PURPOSE

To evaluate the ability of DCE and DWI in differentiating lacrimal gland tumors.

MATERIAL AND METHODS

DCE and DWI were performed in 72 patients with lacrimal gland tumors. Time-intensity curve (TIC) patterns were categorized as type A, type B, type C, and type D. Apparent diffusion coefficient (ADC) was measured on DWI. Then, the diagnostic effectiveness of TIC in conjunction with ADC was assessed using classification and regression tree (CART) analysis.

RESULTS

Type A tumors were all epithelial; they could be further separated into pleomorphic adenoma sand carcinomas. Type B tumors were all non-epithelial tumors, which could be further separated into benign inflammatory infiltrates (BIIs) and lymphomas. Type C tumors contained both carcinomas and non-epithelial tumors, which could be diagnosed into carcinomas, BIIs and lymphomas. Type D tumors were all PAs. The mean ADC of epithelial tumors was significantly higher than that of non-epithelial tumors, and the mean ADC values were significantly different between PAs and carcinomas. Besides, the mean ADC value of BIIs was higher than that of lymphomas. Therefore, the CART decision tree made by ADC and TIC had a predictive accuracy of 86.1%, differentiating lacrimal gland tumors effectively.

CONCLUSION

Combined DCE and DWI-MRI can efficiently differentiate lacrimal gland tumors which can be of help to ophthalmologists in the diagnosis and treatment of these tumors.

Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values

OBJECTIVE

We used radiomics feature-based machine learning classifiers of apparent diffusion coefficient (ADC) maps to differentiate small round cell malignant tumors (SRCMTs) and non-SRCMTs of the nasal and paranasal sinuses.

MATERIALS

A total of 267 features were extracted from each region of interest (ROI). Datasets were randomized into two sets, a training set (∼70%) and a test set (∼30%). We performed dimensional reductions using the Pearson correlation coefficient and feature selection analyses (analysis of variance [ANOVA], relief, recursive feature elimination [RFE]) and classifications using 10 machine learning classifiers. Results were evaluated with a leave-one-out cross-validation analysis.

RESULTS

We compared the AUC for all the pipelines in the validation dataset using FeAture Explorer (FAE) software. The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUCs with ten features. When the "one-standard error" rule was used, FAE produced a simpler model with eight features, including Perc.01%, Perc.10%, Perc.90%, Perc.99%, S(1,0) SumAverg, S(5,5) AngScMom, S(5,5) Correlat, and WavEnLH_s-2. The AUCs of the training, validation, and test datasets achieved 0.995, 0.902, and 0.710, respectively. For ANOVA, the pipeline with the auto-encoder classifier yielded the highest AUC using only one feature, Perc.10% (training/validation/test datasets: 0.886/0.895/0.809, respectively). For the relief, the AUCs of the training, validation, and test datasets that used the LRLasso classifier using five features (Perc.01%, Perc.10%, S(4,4) Correlat, S(5,0) SumAverg, S(5,0) Contrast) were 0.892, 0.886, and 0.787, respectively. Compared with the RFE and relief, the results of all algorithms of ANOVA feature selection were more stable with the AUC values higher than 0.800.

CONCLUSIONS

We demonstrated the feasibility of combining artificial intelligence with the radiomics from ADC values in the differential diagnosis of SRCMTs and non-SRCMTs and the potential of this non-invasive approach for clinical applications.

KEY POINTS

• The parameter with the best diagnostic performance in differentiating SRCMTs from non-SRCMTs was the Perc.10% ADC value. • Results of all the algorithms of ANOVA feature selection were more stable and the AUCs were higher than 0.800, as compared with RFE and relief. • The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUC.

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

Objective

We used texture analysis and machine learning (ML) to classify small round cell malignant tumors (SRCMTs) and Non-SRCMTs of nasal and paranasal sinus on fat-suppressed T2 weighted imaging (Fs-T2WI).

Materials

Preoperative MRI scans of 164 patients from 1 January 2018 to 1 January 2021 diagnosed with SRCMTs and Non-SRCMTs were included in this study. A total of 271 features were extracted from each regions of interest. Datasets were randomly divided into two sets, including a training set (∼70%) and a test set (∼30%). The Pearson correlation coefficient (PCC) and principal component analysis (PCA) methods were performed to reduce dimensions, and the Analysis of Variance (ANOVA), Kruskal-Wallis (KW), and Recursive Feature Elimination (RFE) and Relief were performed for feature selections. Classifications were performed using 10 ML classifiers. Results were evaluated using a leave one out cross-validation analysis.

Results

We compared the AUC of all pipelines on the validation dataset with FeAture Explorer (FAE) software. The pipeline using a PCC dimension reduction, relief feature selection, and gaussian process (GP) classifier yielded the highest area under the curve (AUC) using 15 features. When the “one-standard error” rule was used, FAE also produced a simpler model with 13 features, including S(5,-5)SumAverg, S(3,0)InvDfMom, Skewness, WavEnHL_s-3, Horzl_GlevNonU, Horzl_RLNonUni, 135dr_GlevNonU, WavEnLL_s-3, Teta4, Teta2, S(5,5)DifVarnc, Perc.01%, and WavEnLH_s-2. The AUCs of the training/validation/test datasets were 1.000/0.965/0.979, and the accuracies, sensitivities, and specificities were 0.890, 0.880, and 0.920, respectively. The best algorithm was GP whose AUCs of the training/validation/test datasets by the two-dimensional reduction methods and four feature selection methods were greater than approximately 0.800. Especially, the AUCs of different datasets were greater than approximately 0.900 using the PCC, RFE/Relief, and GP algorithms.

Conclusions

We demonstrated the feasibility of combining artificial intelligence and the radiomics from Fs-T2WI to differentially diagnose SRCMTs and Non-SRCMTs. This non-invasive approach could be very promising in clinical oncology.

Differentiation of Benign From Malignant Sinonasal Masses Using Diffusion Weighted Imaging and Dynamic Contrast Enhanced Magnetic Resonance Imaging

BACKGROUND

The sinonasal region is affected by a variety of neoplasms. A differentiation between benign and malignant masses is essential both for management and prognostication. Morphological analysis often does not allow this differentiation.

OBJECTIVES

This article aims to assess the value of advanced MRI (diffusion [DWI] and dynamic contrast enhanced MRI [DCE-MRI]) in differentiation of benign and malignant sinonasal masses.

METHODS

This prospective study included 40 patients with sinonasal masses who underwent advanced MR on 3T MR scanner. The lesions were analyzed based on morphological characteristics, qualitative, quantitative diffusion parameters, and time signal intensity curves. Apparent diffusion coefficient (ADC) values were acquired using b values of 50 and 1000 s/mm². The accuracy of DWI, DCE-MRI, and combined DWI/DCE-MRI in differentiating benign from malignant sinonasal masses were analyzed.

RESULTS

Perineural extension and growth pattern of the tumor were the best morphological discriminators. Mean ADC values for benign and malignant lesions were 1.675  ±  0.561 and 0.903  ±  0.405  × 10^-3 mm²/sec, ,respectively. ROC revealed that ADC cutoff value of 1.005  × 10^-3 mm²/sec provided an accuracy of 92.5% in differentiating benign from malignant masses (P value <.01). On excluding the benign vascular masses (Juvenile Nasopharyngeal Angiofibroma and hemangioma), the time signal intensity curve showed 78% accuracy (P value <.001). The highest diagnostic performance was achieved by combining DWI and DCE-MRI (95% accuracy).

CONCLUSION

DWI has higher accuracy than DCE-MRI. Quantitative DWI is preferable over qualitative DWI. Accuracy of DCE-MRI can be increased by excluding vascular masses with characteristic imaging features. DWI and DCE-MRI have the highest accuracy when used in combination than either of them alone in differentiating benign from malignant sinonasal masses.

Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

Background

This study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).

Methods

Thirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm²) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADC_mean, variance, skewness, kurtosis, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀, and ADC₉₉) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer-Lemeshow test.

Results

NPL exhibited significantly lower ADC_mean, variance, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀ and ADC₉₉, when compared to NPC (all, P < 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC₉₉ revealed the highest diagnostic efficiency, followed by ADC₁₀ and ADC₂₀. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC₉₉ = 1,485.0 × 10^-6 mm²/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%).

Conclusion

Whole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

An MRI-Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

BACKGROUND

Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection.

PURPOSE

To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors.

STUDY TYPE

Retrospective.

POPULATION

In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors.

FIELD STRENGTH/SEQUENCES

Fast-spin-echo (FSE) T₁ -weighted and fat-suppressed FSE T₂ -weighted imaging on a 1.5T and 3.0T MRI.

ASSESSMENT

T₁ and fat-suppressed T₂ -weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI).

STATISTICAL TESTS

Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test.

RESULTS

In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model.

DATA CONCLUSION

The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

The Specific Magnetic Resonance Imaging Indicators in Predicting Clear-Cell Renal Cell Carcinoma Metastatic to the Sinonasal Region

PURPOSE

The aim of this study was to determine the valuable magnetic resonance imaging (MRI) features of sinonasal metastatic clear-cell renal cell carcinoma (cc-RCC), especially focusing on its dynamic-enhanced characteristics.

METHODS

The conventional and dynamic-enhanced MRI findings of 8 patients with histopathologically confirmed sinonasal metastatic cc-RCC were reviewed by 2 radiologists. The control group of 8 patients with capillary hemangioma underwent the same MRI protocol.

RESULTS

Metastatic cc-RCCs arose from the nasoethmoid region, maxillary sinus, posterior ethmoid and sphenoid sinus, and nasal cavity in 2 patients in each. These lesions were well circumscribed and the mean maximum dimension was 42 mm. The signal intensity of these lesions was isointense to brain stem on both MR T1- and T2-weighted images. All metastatic tumors showed vivid enhancement on enhanced T1-weighted image. Multiple flow voids within these metastatic lesions were identified in 6 patients. Peripheral cyst was detected around the metastatic tumor in 4 patients. Metastatic cc-RCCs exhibited a characteristic type 4 time intensity curve (TIC) similar to that of the internal carotid artery, whereas capillary hemangiomas showed a type 3 TIC on dynamic-enhanced MRI.

CONCLUSIONS

A hypervascular mass with the characteristic type 4 TIC in the sinonasal region is highly suggestive of a metastatic cc-RCC.

Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion in Differentiating Nasal Malignancies

OBJECTIVES/HYPOTHESIS

To evaluate the usefulness of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in the differentiation of sinonasal malignant tumors (SNMTs) with different histological types.

STUDY DESIGN

Retrospective observational and diagnostic study.

METHODS

Sixty-five patients with SNMTs who underwent DKI and IVIM were enrolled in this retrospective study, including 27 squamous cell carcinomas (SCCs), 13 olfactory neuroblastomas (ONBs), 14 malignant melanomas (MMs) and 11 lymphomas. The kurtosis (K) and diffusion coefficient (Dk) from DKI and the pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and the product of D* and f (f∙D*) from IVIM were measured. Kruskal-Wallis and Dunn multiple comparison tests with Bonferroni correction, receiver operating characteristic curve, and logistic regression analyses were used for statistical analysis.

RESULTS

Lymphomas demonstrated the highest K values but lowest Dk, D, D*, f, and f∙D* values among these four malignant tumors. ONBs exhibited high K values and MMs had highest D*, f, and f∙D* values. The cutoff value of ≤0.887 × 10^-3 mm² /sec for f∙D* provided a sensitivity, specificity, and an accuracy of 100%, 98.1%, and 98.5%, respectively, for differentiating lymphomas from the other three entities. The combination of f∙D* and D values showed a sensitivity of 92.9% and a specificity of 92.5% for the discrimination of MMs from ONBs and SCCs. The K value was useful for differentiating ONBs from SCCs, with a threshold value of 0.942 (sensitivity, 84.6%; specificity, 63.0%).

CONCLUSIONS

The combined use of DKI and IVIM is helpful for differentiating among four histological types of SNMTs.

LEVEL OF EVIDENCE

3 Laryngoscope, 2019.

Whole-tumor histogram analysis of monoexponential and advanced diffusion-weighted imaging for sinonasal malignant tumors: Correlations with histopathologic features

BACKGROUND

The histopathological basis of monoexponential diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) in the characterization of sinonasal malignant tumors is still unclear.

PURPOSE

To explore the correlations of histogram metrics from monoexponential DWI, IVIM, and DKI with histopathologic features in sinonasal malignant tumors.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 76 patients with sinonasal malignant tumors.

FIELD STRENGTH/SEQUENCE

Fourteen different b values (b = 0, 50, 100, 150, 200, 250, 300, 350, 400, 800, 1000, 1500, 2000, and 2500 sec/mm² ) were used to perform different DWI models at 3.0T.

ASSESSMENT

The whole-tumor histogram metrics were calculated on the apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), diffusion kurtosis (K), and diffusion coefficient (Dk) maps. Histopathologic features, including nuclear, cytoplasmic, cellular, stromal fractions, and the nuclear-to-cytoplasmic (N/C) ratio, were measured.

STATISTICAL TESTS

Spearman correlations and stepwise multiple linear regression analyses were performed to determine the correlations between histogram metrics and histopathologic features.

RESULTS

ADC, Dk, and f histogram metrics showed significant correlations with investigated histopathologic features; D and K histogram metrics were significantly correlated with cellular, stromal, and nuclear fractions (all P < 0.05). Significant correlations between the 75th percentile of D and cytoplasmic fraction and between the kurtosis of K and the N/C ratio were observed (P < 0.05). The skewness of Dk, K, and the 75th percentile of D were independently associated with cellular and nuclear fractions; the skewness of Dk and K were independently associated with stromal fraction (P < 0.05).

DATA CONCLUSION

Monoexponential and advanced DWI histogram parameters were significantly correlated with histopathologic features in sinonasal malignancies.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:273-285.

The value of the apparent diffusion coefficient calculated from diffusion-weighted magnetic resonance imaging scans in the differentiation of maxillary sinus inflammatory diseases

OBJECTIVES

This study examined the value of the apparent diffusion coefficient (ADC) calculated by using diffusion-weighted imaging (DWI) in the differentiation of inflammatory lesions of the maxillary sinus.

STUDY DESIGN

Sixty-five maxillary sinus magnetic resonance imaging (MRI) scans with findings suggestive of inflammatory lesions were qualitatively categorized into 4 distinct groups by using T2-weighted images: group 1: presence of mucosal thickening; group 2: presence of sinonasal polyps or mucous retention cysts; group 3: presence of fluid identified by air-fluid levels with a homogeneous signal intensity; and group 4: presence of fluid identified by air-fluid levels with a heterogeneous signal intensity. The ADC of each imaging finding was measured by using a 5-mm area of interest. Statistical differences between the groups were determined by using nonparametric tests with a 5% significance level.

RESULTS

Statistically significant differences were observed between group 1 and the other groups. Mucosal thickening was associated with lower ADC values compared with the other inflammatory lesions.

CONCLUSIONS

The ADC can be useful in differentiating mucosal thickening from other inflammatory alterations in the maxillary sinuses. Mucosal thickening had more restricted water diffusion compared with the other inflammatory lesions.

Diffusion-weighted magnetic resonance imaging of the paranasal sinuses: A systematic review

OBJECTIVES

This was a systematic review of studies on the use of diffusion-weighted imaging (DWI) for paranasal sinus diseases. The applications of DWI were analyzed along with the main results, and conclusions were obtained by the investigators.

STUDY DESIGN

Databases were searched using the keyword "diffusion" combined with "sinonasal," "paranasal sinus," "maxillary sinus," "frontal sinus," "ethmoid sinus," and "sphenoid sinus," including only articles that were published from 2008 to 2018. Only original English language studies with sinonasal disease samples were selected.

RESULTS

Sixteen studies about various sinonasal diseases were included. The main objectives of most of the studies were related to the use of the apparent diffusion coefficient (ADC) in the differentiation of benign lesions and malignant neoplasms. We concluded that the ADC for malignant neoplasms is lower. Histologic features of samples evaluated in the studies were heterogeneous.

CONCLUSIONS

The ADC may improve the quality of the diagnostic hypothesis, particularly in differentiating benign and malignant diseases. Furthermore, the differences between certain types of lesions could be determined by using the ADC. However, further studies focusing on inflammatory diseases should be performed. Overall, DWI and the ADC are promising methods that can be incorporated into routine evaluations.

Diffusion-Weighted Magnetic Resonance Imaging in Maxillary Sinuses Inflammatory Diseases: Report of Three Cases and Literature Review

Background

Magnetic resonance imaging is considered a preferable imaging examination in the diagnosis of inflammatory maxillary sinus disease and can provide precise sinonasal characterization. Diffusion-weighted magnetic resonance imaging and apparent diffusion coefficient are complementary magnetic resonance imaging tools that can be applied to the differentiation of sinus diseases. In this report, 3 cases of inflammatory maxillary sinus diseases imaging findings considering diffusion-weighted magnetic resonance imaging features were described. Additionally, a literature review considering the use of diffusion-weighted magnetic resonance imaging in inflammatory lesions is provided.

Methods

The cases reported were: presence of air-fluid levels, mucosal thickening and a mucous retention cyst. Conventional magnetic resonance imaging and apparent diffusion coefficient (ADC) maps, with ADC values were demonstrated. In the literature review, the studies considering inflammatory lesions were detailed, as well as ADC values established by investigators.

Results

ADC values for presence of air-fluid levels, mucosal thickening and mucous retention cyst were respectively: 1.99 x 10^-3 mm²/s; 1.83 x 10^-3 mm²/s; 2.05 x 10^-3 mm²/s.

Conclusions

It was observed that apparent diffusion coefficient values from the inflammatory lesions described in this report were different and apparent diffusion coefficient may be useful in the differentiation of these maxillary sinus alterations. Further larger sample investigations considering apparent diffusion coefficient values focusing in inflammatory lesions are recommended. The lack of studies considering the use of diffusion-weighted magnetic resonance imaging on inflammatory diseases diagnostic was the major limitation to the literature review.

Intravoxel Incoherent Motion MR Imaging in the Differentiation of Benign and Malignant Sinonasal Lesions: Comparison with Conventional Diffusion-Weighted MR Imaging

BACKGROUND AND PURPOSE

Intravoxel incoherent motion is a promising method for the differentiation of sinonasal lesions. This study aimed to evaluate the value of intravoxel incoherent motion in the differentiation of benign and malignant sinonasal lesions and to compare the diagnostic performance of intravoxel incoherent motion with that of conventional DWI.

MATERIALS AND METHODS

One hundred thirty-one patients with histologically proved solid sinonasal lesions (56 benign and 75 malignant) who underwent conventional DWI and intravoxel incoherent motion were recruited in this study. The diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values derived from intravoxel incoherent motion and ADC values derived from conventional DWI were measured and compared between the 2 groups using the Student t test. Receiver operating characteristic curve analysis, logistic regression analysis, and 10-fold cross-validation were performed to evaluate the diagnostic performance of single-parametric and multiparametric models.

RESULTS

The mean ADC and D values were significantly lower in malignant sinonasal lesions than in benign sinonasal lesions (both P < .001). The mean f value was higher in malignant lesions than in benign lesions (P = .003). Multiparametric models can significantly improve the cross-validated areas under the curve for the differentiation of sinonasal lesions compared with single-parametric models (all corrected P < .05 except the D value). The model of D+f provided a better diagnostic performance than the ADC value (corrected P < .001).

CONCLUSIONS

Intravoxel incoherent motion appears to be a more effective MR imaging technique than conventional DWI in the differentiation of benign and malignant sinonasal lesions.

Can Diffusion Weighted Imaging Aid in Differentiating Benign from Malignant Sinonasal Masses?: A Useful Adjunct

Background

To evaluate the role of diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) values at 3 Tesla in characterizing sinonasal masses.

Material/Methods

After ethical clearance, 79 treatment naive patients with head and neck masses underwent magnetic resonance imaging (MRI), including DWI at 3 Tesla using the following b values – 0, 500 and 1000 s/mm². Thirty-one patients were found to have sinonasal tumours and were subsequently analysed. Image analysis consisted of a morphological evaluation of conventional MR images, qualitative evaluation of DW trace images and quantitative assessment of mean ADC values. Receiver operating characteristic (ROC) curve was drawn to determine a cut-off ADC value for the differentiation between benign and malignant masses.

Results

Sinonasal masses showed an overlapping growth pattern on conventional imaging, irrespective of their biological nature. The mean ADC value of benign lesions was 1.948±0.459×10⁻³ mm²/s, while that of malignant lesions was 1.046±0.711×10⁻³ mm²/s, and the difference was statistically significant (p=0.004). When a cut-off ADC value of 1.791×10⁻³ mm²/s was used, sensitivity of 80% and specificity of 83.3% were obtained for characterization of malignant lesions, which was statistically significant. Juvenile nasopharyngeal angiofibroma (JNA) showed distinctly high ADC values, while meningioma was the only benign lesion with restricted diffusion. Atypical entities with unexpected diffusion characteristics included: adenocarcinoma, adenoid cystic carcinoma, meningioma, chondrosarcoma and fibromyxoid sarcoma.

Conclusions

DWI in conjunction with conventional imaging can potentially enhance the diagnostic accuracy in characterizing sinonasal masses as benign or malignant. Some specific entities such as JNA and meningioma showed distinctive diffusion characteristics.

High-Resolution Diffusion-Weighted Imaging Improves the Diagnostic Accuracy of Dynamic Contrast-Enhanced Sinonasal Magnetic Resonance Imaging

OBJECT

The aim of the study was to evaluate the incremental value of high-resolution diffusion-weighted imaging and apparent diffusion coefficient (ADC) mapping compared with that of dynamic contrast-enhanced (DCE) sinonasal magnetic resonance imaging (MRI) in the characterization of benign versus malignant sinonasal tumors at 3.0T.

MATERIALS AND METHODS

Ninety-eight patients (61 females, 37 males; mean age, 48 years) with sinonasal lesions who underwent high-resolution DW-MRI and DCE-MRI were included in this study. The lesions were divided into malignant and benign groups on the basis of pathological examination. In total, 58 malignant tumors and 40 benign tumors were evaluated. Apparent diffusion coefficients were acquired with 0 and 1000 s/mm b values. Semiquantitative parameters (time-signal intensity curve [TIC] type, time of peak enhancement within the first 2 minutes [Tpeak], peak percentage enhancement within the first 2 minutes [PEpeak], the last time point percentage enhancement [PElast], and the washout percentage-enhancement difference [PEwashout]) were derived from DCE-MRI. The DCE-MRI parameters and ADCs were included in multivariate models to predict a diagnosis of a benign versus malignant lesion.

RESULTS

The accuracy using semiquantitative DCE-MRI parameters alone was 70.4% (0.693 area under the ROC curve, 57.5% sensitivity, 79.3% specificity). Adding absolute ADCs to dynamic contrast-enhanced MR data showed the higher diagnostic accuracy of 85.7% (0.873 area under the ROC curve, 85.0% sensitivity, 86.2% specificity). Moreover, the absolute ADCs differed significantly between the benign (mean [SD] ADC, 1.211 [0.32] × 10 mm/s) and malignant (mean [SD] ADC, 0.702 [0.28] × 10 mm/s) sinonasal tumors (P < 0.001). In addition, a significant difference was found between the ADC values of malignant and benign lesions in washout enhancement TICs (t = 7.039, P < 0.001).

CONCLUSIONS

High-resolution DWI with ADC mapping significantly improved the diagnostic accuracy of dynamic contrast-enhanced sinonasal MRI at 3.0T. In addition, ADC values could distinguish benign lesions from malignant tumors in washout enhancement TICs.

Current Trends in Sinonasal Imaging

As endoscopic sinus surgery (ESS) has evolved since its introduction to the United States, so has technology for imaging the sinonasal cavities. Although imaging is most frequently performed for evaluating chronic sinusitis refractory to medical therapy, its uses have expanded beyond inflammatory sinus disease. Multidetector Computed Tomography is the current workhorse for both diagnosis and preoperative planning in prospective ESS patients, while MR imaging remains a complementary tool for evaluating suspected tumors or intracranial and orbital complications of rhinosinusitis. In this article, the authors review current trends and potential future directions in the use of these modalities for sinus imaging.

Role of quantitative magnetic resonance imaging parameters in the evaluation of treatment response in malignant tumors

OBJECTIVE

To elaborate the role of quantitative magnetic resonance imaging (MRI) parameters in the evaluation of treatment response in malignant tumors.

DATA SOURCES

Data cited in this review were obtained mainly from PubMed in English from 1999 to 2014, with keywords "dynamic contrast-enhanced (DCE)-MRI," "diffusion-weighted imaging (DWI)," "microcirculation," "apparent diffusion coefficient (ADC)," "treatment response" and "oncology."

STUDY SELECTION

Articles regarding principles of DCE-MRI, principles of DWI, clinical applications as well as opportunity and aspiration were identified, retrieved and reviewed.

RESULTS

A significant correlation between ADC values and treatment response was reported in most DWI studies. Most quantitative DCE-MRI studies showed a significant correlation between K trans values and treatment response. However, in different tumors and studies, both high and low pretreatment ADC or K trans values were found to be associated with response rate. Both DCE-MRI and DWI demonstrated changes in their parameters hours to days after treatment, showing a decrease in K trans or an increase in ADC associated with response in most cases.

CONCLUSIONS

Combinations of quantitative MRI play an important role in the evaluation of treatment response of malignant tumors and hold promise for use as a cancer treatment response biomarker. However, validation is hampered by the lack of reproducibility and standardization. MRI acquisition protocols and quantitative image analysis approaches should be properly addressed prior to further testing the clinical use of quantitative MRI parameters in the assessment of treatments.

Improved performance in differentiating benign from malignant sinonasal tumors using diffusion-weighted combined with dynamic contrast-enhanced magnetic resonance imaging

Background:

Differentiating benign from malignant sinonsal lesions is essential for treatment planning as well as determining the patient's prognosis, but the differentiation is often difficult in clinical practice. The study aimed to determine whether the combination of diffusion-weighted (DW) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can improve the performance in differentiating benign from malignant sinonasal tumors.

Methods:

This retrospective study included 197 consecutive patients with sinonasal tumors (116 malignant tumors and 81 benign tumors). All patients underwent both DW and DCE-MRI in a 3-T magnetic resonance scanner. Two different settings of b values (0,700 and 0,1000 s/mm²) and two different strategies of region of interest (ROI) including whole slice (WS) and partial slice (PS) were used to calculate apparent diffusion coefficients (ADCs). A DW parameter with WS ADCs_b0,1000 and two DCE-MRI parameters (time intensity curve [TIC] and time to peak enhancement [Tpeak]) were finally combined to use in differentiating the benign from the malignant tumors in this study.

Results:

The mean ADCs of malignant sinonasal tumors (WS ADCs_b0,1000 = 1.084 × 10⁻³ mm²/s) were significantly lower than those of benign tumors (WS ADCs_b0,1000 = 1.617 × 10⁻³ mm²/s, P < 0.001). The accuracy using WS ADCs_b0,1000 alone was 83.7% in differentiating the benign from the malignant tumors (85.3% sensitivity, 81.2% specificity, 86.4% positive predictive value [PPV], and 79.5% negative predictive value [NPV]). The accuracy using DCE with Tpeak and TIC alone was 72.1% (69.1% sensitivity, 74.1% specificity, 77.5% PPV, and 65.1% NPV). Using DW-MRI parameter was superior than using DCE parameters in differentiation between benign and malignant sinonasal tumors (P < 0.001). The accuracy was 87.3% (90.5% sensitivity, 82.7% specificity, 88.2% PPV, and 85.9% NPV) using DW-MRI combined with DCE-MRI, which was superior than that using DCE-MRI alone or using DW-MRI alone (both P < 0.001) in differentiating the benign from the malignant tumors.

Conclusions:

Diffusion-weighted combined with DCE-MRI can improve imaging performance in differentiating benign from malignant sinonasal tumors, which has the potential to improve diagnostic accuracy and to provide added value in the management for these tumors.

Imaging Approach to Sinonasal Neoplasms

The neoplastic lesions that can be found in the sinonasal space are numerous and varied. This article describes a general approach to narrowing down the differential diagnosis and provides the treating physician sufficient information to choose and deliver the best treatment modality. Computed tomography and MR imaging together provide complete radiologic assessment of sinonasal neoplasms.

Differentiation of benign and malignant lesions of the tongue by using diffusion-weighted MRI at 3.0 T

OBJECTIVES

Diffusion-weighted MRI (DWI) has been introduced in head and neck lesions and adds important information to the findings obtained through conventional MRI. The purpose of this study was to assess the role of DWI in differentiating benign and malignant lesions of the tongue at 3.0-T field strength imaging.

METHODS

78 patients with 78 lingual lesions underwent conventional MRI and DWI with b-values of 0 and 1000 s mm(-2) before therapy. The apparent diffusion coefficient (ADC) maps were reconstructed, and the ADC values of the lingual lesions were calculated and compared between benign and malignant lesions of the tongue.

RESULTS

The mean ADC values of the malignant tumours, benign solid lesions and cystic lesions were (1.08±0.16)×10(-3), (1.68±0.33)×10(-3) and (2.21±0.35)×10(-3) mm2 s(-1), respectively. The mean ADC values of malignant tumours were significantly lower (p<0.001) than those of benign solid lesions, and the mean ADC values of benign solid lesions were significantly lower (p<0.001) than those of cystic lesions. Receiver operating characteristic analysis showed that when an ADC value<.31×10(-3) mm2 s(-1) was used for predicting malignancy, the highest accuracy of 95.3%, sensitivity of 92.6% and specificity of 97.3% were obtained.

CONCLUSIONS

ADC values of benign and malignant lesions are significantly different at 3.0-T imaging. DWI can be applied as a complementary tool in the differentiation of benign and malignant lesions of the tongue.

Magnetic resonance imaging in sinonasal disease

Computed tomography (CT) is often the primary imaging modality for the evaluation of sinonasal disease. For some indications, magnetic resonance imaging (MRI) may provide additional information. There are established indications for using MRI in complicated sinonasal inflammatory disease, invasive fungal sinus disease, and sinonasal mass lesions. When MRI is used in the evaluation of sinonasal disease, it is usually used as a complementary modality in addition to CT. Magnetic resonance imaging in sinonasal disease can be used to further characterize the primary sinonasal disease process and to evaluate the extent of complications such as orbital or intracranial involvement. When MRI is used in sinonasal disease, it should be evaluated in the context of the clinical situation and CT imaging features. This will help radiologists provide a meaningful differential diagnosis to assist in clinical management.

Routine and dynamic MR imaging study of lobular capillary hemangioma of the nasal cavity with comparison to inverting papilloma

BACKGROUND AND PURPOSE

Lobular capillary hemangioma is an uncommon lesion, and its MR imaging appearance has not been fully characterized. The purpose of this study was to determine the MR imaging features of nasal lobular capillary hemangioma and contrast its imaging characteristics to inverting papilloma.

MATERIALS AND METHODS

The MR imaging signals of 32 patients with histopathologically proven nasal lobular capillary hemangiomas and 53 patients with nasal inverted papillomas were retrospectively studied. The findings of dynamic contrast-enhanced MR imaging in 24 lobular capillary hemangiomas and in 53 inverted papillomas were also analyzed. The Monte Carlo exact test was used for comparison of the time-intensity curve patterns of lobular capillary hemangioma and inverted papilloma.

RESULTS

All lobular capillary hemangiomas appeared to be homogeneously isointense to gray matter on T1-weighted images. On T2-weighted images, all lesions showed heterogeneous hyperintensity, and a thin peripheral isointense or hypointense ring was seen in 28 patients. All lesions showed marked enhancement on enhanced images, with the exception of enhancement of the T2 isointense or hypointense ring. Forty-three (81.1%) inverted papillomas had moderate heterogeneous T2 signal intensity, and a characteristic "cerebriform" appearance was detected in 45 (84.91%) of 53 inverted papillomas. The time-intensity curves showed a washout pattern in 18 and a plateau pattern in 6 patients, whereas inverted papillomas showed a washout pattern in 12, a plateau pattern in 35, and a persistent pattern in 6 patients. There was a statistically significant difference as to time-intensity curve pattern between the 2 groups (P < .05).

CONCLUSIONS

Hyperintensity on T2-weighted images, marked enhancement of tumor with a nonenhancing thin peripheral ring, and a washout time-intensity curve pattern are characteristic MR imaging features of nasal lobular capillary hemangiomas.

Head and neck tumours: combined MRI assessment based on IVIM and TIC analyses for the differentiation of tumors of different histological types

OBJECTIVES

We evaluated the combined use of intravoxel incoherent motion (IVIM) and time-signal intensity curve (TIC) analyses to diagnose head and neck tumours.

METHODS

We compared perfusion-related parameters (PP) and molecular diffusion values (D) determined from IVIM theory and TIC profiles among 92 tumours with different histologies.

RESULTS

IVIM parameters (f and D values) and TIC profiles in combination were distinct among the different types of head and neck tumours, including squamous cell carcinomas (SCCs), lymphomas, malignant salivary gland tumours, Warthin's tumours, pleomorphic adenomas and schwannomas. A multiparametric approach using both IVIM parameters and TIC profiles differentiated between benign and malignant tumours with 97 % accuracy and diagnosed different tumour types with 89 % accuracy.

CONCLUSIONS

Combined use of IVIM parameters and TIC profiles has high efficacy in diagnosing head and neck tumours.

KEY POINTS

• Head and neck tumours have wide MR perfusion/diffusion properties. • Dynamic contrast-enhanced (DCE) MR imaging can characterise tumour perfusion (TIC analysis). • Intravoxel incoherent motion (IVIM) imaging can provide diffusion and perfusion properties. • However, IVIM or DCE imaging alone is insufficient for diagnosing head/neck tumours. • Multiparametric approach using both IVIM and TIC profiles can facilitate the diagnosis.