Effectiveness of 3 T PROPELLER DUO diffusion-weighted MRI in differentiating sinonasal lymphomas and carcinomas

Value of multiparametric magnetic resonance imaging in distinguishing sinonasal lymphoma from sinonasal carcinoma: a case control study

BACKGROUND

The study aimed to evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in distinguishing sinonasal lymphoma from sinonasal carcinoma.

METHODS

Forty-two participants with histologically confirmed sinonasal lymphomas and fifty-two cases of sinonasal carcinoma underwent imaging with a 3.0T MRI scanner. DCE-MRI and DWI were conducted, and various parameters including type of time-intensity curve(TIC), time to peak, peak enhancement, peak contrast enhancement, washout rate, apparent diffusion coefficient (ADC), and relative ADC were measured. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the diagnostic capability of individual and combined indices for differentiating nasal sinus lymphoma from nasal sinus carcinoma.

RESULTS

Sinonasal lymphoma predominantly exhibited type II TIC(n = 20), whereas sinonasal carcinoma predominantly exhibited type III TIC(n = 23). Significant differences were observed in all parameters except washout ratio (p < 0.05), and ADC value emerged as the most reliable diagnostic tool in single parameter. Combined DCE-MRI parameters demonstrated superior diagnostic efficacy compared to individual parameters, with the highest efficiency (area under curve = 0.945) achieved when combining all parameters of DCE-MRI and DWI.

CONCLUSIONS

Multiparametric evaluation involving contrast-enhanced dynamic MRI and DWI holds considerable diagnostic value in distinguishing sinonasal lymphoma from sinonasal carcinoma.

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

BACKGROUND

Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field.

METHODS

In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication.

RESULTS

The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention.

CONCLUSION

As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.

Diffusion Tensor Imaging of Peripheral Nerves: Current Status and New Developments

Diffusion tensor imaging (DTI) is an emerging technique for peripheral nerve imaging that can provide information about the microstructural organization and connectivity of these nerves and complement the information gained from anatomical magnetic resonance imaging (MRI) sequences. With DTI it is possible to reconstruct nerve pathways and visualize the three-dimensional trajectory of nerve fibers, as in nerve tractography. More importantly, DTI allows for quantitative evaluation of peripheral nerves by the calculation of several important parameters that offer insight into the functional status of a nerve. Thus DTI has a high potential to add value to the work-up of peripheral nerve pathologies, although it is more technically demanding. Peripheral nerves pose specific challenges to DTI due to their small diameter and DTI's spatial resolution, contrast, location, and inherent field inhomogeneities when imaging certain anatomical locations. Numerous efforts are underway to resolve these technical challenges and thus enable wider acceptance of DTI in peripheral nerve MRI.

PET/CT Imaging in Treatment Planning and Surveillance of Sinonasal Neoplasms

Sinonasal cancers are uncommon malignancies with a generally unfavorable prognosis, often presenting at an advanced stage. Their high rate of recurrence supports close imaging surveillance and the utilization of functional imaging techniques. Whole-body 18F-FDG PET/CT has very high sensitivity for the diagnosis of sinonasal malignancies and can also be used as a "metabolic biopsy" in the characterization of some of the more common subgroups of these tumors, though due to overlap in uptake, histological confirmation is still needed. For certain tumor types, radiotracers, such as 11C-choline, and radiolabeled somatostatin analogs, including 68Ga-DOTATATE/DOTATOC, have proven useful in treatment planning and surveillance. Although serial scans for posttreatment surveillance allow the detection of subclinical lesions, the optimal schedule and efficacy in terms of survival are yet to be determined. Pitfalls of 18F-FDG, such as post-surgical and post-radiotherapy crusting and inflammation, may cause false-positive hypermetabolism in the absence of relapse.

Differentiating sinonasal malignant melanoma from squamous cell carcinoma using DWI combined with conventional MRI

PURPOSE

This study aimed to investigate the feasibility of diffusion-weighted imaging (DWI) in combination with conventional MRI features to differentiate sinonasal malignant melanoma (SNMM) from sinonasal squamous cell carcinoma (SNSCC).

METHODS

A total of 37 patients with SNMM and 44 patients with SNSCC were retrospectively reviewed. Conventional MRI features and apparent diffusion coefficients (ADCs) were evaluated independently by two experienced head and neck radiologists. ADCs were obtained from two different regions of interest (ROIs) including maximum slice (MS) and small solid sample (SSS). Multivariate logistic regression analysis was performed to identify significant MR imaging features in discriminating between SNMM and SNSCC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance.

RESULTS

SNMMs were more frequently located in the nasal cavity, with well-defined border, T1 Septate Pattern (T1-SP) and heterogeneous T1 hyperintensity, whereas SNSCCs were more frequently located in the paranasal sinus, with homogenous T1 isointensity, ill-defined border, reticular or linear T2 hyperintensity, and pterygopalatine fossa or orbital involvement (all p < 0.05). The mean ADCs of SNMM (MS ADC, 0.85 × 10^-3mm²/s; SSS ADC, 0.69 × 10^-3mm²/s) were significantly lower than those of SNSCC (MS ADC, 1.05 × 10^-3mm²/s; SSS ADC, 0.82 × 10^-3mm²/s) (p < 0.05). With a combination of location, T1 signal intensity, reticular or linear T2 hyperintensity, and a cut-off MS ADC of 0.87 × 10^-3mm²/s, the sensitivity, specificity, and AUC were 97.3%, 68.2%, and 0.89, respectively.

CONCLUSION

DWI combined with conventional MRI can effectively improve the diagnostic performance in differentiating SNMM from SNSCC.

Investigation of the Value of T 2 Mapping in the Prediction of Eosinophilic Chronic Rhinosinusitis With Nasal Polyps

OBJECTIVES

Patients with eosinophilic chronic rhinosinusitis with nasal polyps (eosCRSwNP) usually have more extensive sinus disease, severe symptoms, and poorer disease control compared with patients with non-eosCRSwNP. Separating these entities will be crucial for patient management. The purpose of this study is to investigate T 1, T 2 , and apparent diffusion coefficient (ADC) values of the nasal polyps in patients with CRSwNP and evaluate the usefulness of these parameters for differentiating these diseases.

METHODS

Sinonasal magnetic resonance imaging was performed in 36 patients with eosCRSwNP and 20 patients with non-eosCRSwNP (including T 1 mapping, T 2 mapping, and diffusion-weighted imaging) before surgery. The T 1 , T 2 , and ADC values were calculated and correlated with pathologically assessed inflammatory cells of nasal polyps.

RESULTS

Significant higher T 2 value, higher eosinophil count, and lower lymphocyte count of the nasal polyps were observed in eosCRSwNP than those in non-eosCRSwNP. There was no significant difference in T 1 or ADC values between the 2 groups. T 2 value was correlated with eosinophil count and lymphocyte count in CRSwNP. The area under the curve of T 2 value for predicting eosCRSwNP was 0.78 with 89.9% sensitivity and 60.0% specificity.

CONCLUSION

T 2 value is a promising imaging biomarker for predicting eosCRSwNP. It can help to distinguish eosCRSwNP from non-eosCRSwNP.

Differentiation between atypical sinonasal non-Hodgkin's lymphoma and inverted papilloma

AIM

To seek additional magnetic resonance imaging (MRI) features to improve the accuracy of differentiation between atypical sinonasal non-Hodgkin's lymphoma (NHL) and inverted papilloma (IP) using conventional MRI and apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

MRI examinations from 44 atypical cases (21 NHLs and 23 IPs) in sinonasal regions were reviewed retrospectively. Imaging features included tumour laterality, extension, T1-weighted imaging (WI)/T2WI signal intensity homogeneity and ratios, enhancement homogeneity and ratios, and ADC_mean.

RESULTS

In cases of NHL, homogeneous signal intensity was often observed on T2WI, which was homogeneous and significantly less enhanced than the turbinate, with lower ADC_mean. Whereas in IPs, heterogeneous signal intensity was seen on T2WI, which was heterogeneous and of comparable enhancement to the turbinate, and higher ADC_mean values were commonly seen. An ADC_mean cut-off point of 1.10 × 10^-3 mm²/s achieved 100% sensitivity, 90% specificity, and 90% accuracy. In addition, special features were observed that support the distinction between the two tumours, including intestinal pattern enhancement in NHL and spot-like appearance on T2WI and enhancement in IP.

CONCLUSIONS

ADC_mean was the most valuable metric for differentiating between the atypical sinonasal NHLs and IPs.

Sinonasal lymphoma: A primer for otolaryngologists

Objective

Sinonasal lymphomas are a rare entity that commonly present with nonspecific sinonasal symptoms and are often recognized immediately. Through this review, we aim to summarize important principles in diagnosis and treatment of sinonasal lymphomas, with the goal of disseminating the current knowledge of this under-recognized malignancy to otolaryngologists.

Methods

Systemic review using PRISMA guidelines of foundational scholarly articles, guidelines, and trials were reviewed focusing on clinical characteristics of key sinonasal lymphoma subtypes, along with available treatments in the otolaryngology, medical oncology, and radiation oncology literature.

Results

Sinonasal lymphoma are derived from clonal proliferation of lymphocytes at various stages of differentiation, of which diffuse large B-cell lymphoma (DLBCL) and extranodal natural killer/T-cell lymphoma (ENKTL) are the most common. Diagnosis and staging require biopsy with immunohistochemistry in conjunction with imaging and laboratory studies. Treatment is ever evolving and currently includes multi-agent chemotherapy and/or radiation therapy.

Conclusion

Otolaryngologists may be the first to recognize sinonasal lymphoma, which requires a comprehensive workup and a multidisciplinary team for treatment. Symptoms are nonspecific and similar to many sinonasal pathologies, and it is crucial for otolaryngologists to keep a broad differential.

Level of Evidence

5.

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.

Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas

PURPOSE

To develop and validate an MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas (SCCs).

MATERIALS AND METHODS

One-hundred-and-fifty-four patients were enrolled (74 individuals with SCCs and 80 with lymphomas). After feature analysis and feature selection with variance threshold and least absolute shrinkage and selection operator (LASSO) methods, an MRI-based radiomics model with the support vector machine (SVM) classifier was constructed in differentiation between lymphomas and SCCs. Areas under the receiver operating characteristic curves (AUCs) of the MRI-based radiomics model were compared with those of radiologists using Delong test.

RESULTS

Five features (T1 original shape Compactness2, T1 wavelet-HHH first-order Total Energy, T2 wavelet-HLH GLCM Informational Measure of Correlation1, T1 wavelet-LHL GLCM Inverse Variance and T1 square GLRLM Long Run Low Gray Level Emphasis) were finally selected in the radiomics model. The AUC values in differentiation between lymphomas and SCCs were 0.94 for the training dataset and 0.85 for the validation dataset, respectively. For all the patient datasets, the AUC values of radiomics model, readers 1, 2 and 3 were 0.92, 0.76, 0.77 and 0.80, respectively. For the validation datasets, no significant difference was found between the AUCs of the radiomics model and those of the three radiologist (P = 0.459, 0.469, 0.738 for radiologist 1, 2 and 3, respectively).

CONCLUSION

An MRI-based radiomics model can help to differentiate sinonasal lymphomas from SCCs with high accuracy.

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.

Differentiation between sinonasal natural killer/T-cell lymphomas and diffuse large B-cell lymphomas by RESOLVE DWI combined with conventional MRI

OBJECTIVE

To explore the feasibility of using RESOLVE DWI combined with conventional magnetic resonance imaging (MRI) to discriminate between sinonasal NKTLs and DLBCLs and to investigate the correlation between ADC value and Ki-67 expression in the two subtypes of NHLs.

MATERIALS AND METHODS

Sixty patients with NKTLs and twenty-six patients with DLBCLs in the sinonasal region who were confirmed by histopathology underwent high-resolution DWI and conventional MRI. The apparent diffusion coefficients (ADCs) and conventional MRI features associated with NKTLs and DLBCLs were compared using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC) values for conventional MRI and MRI in combination with DWI were compared to determine the diagnostic performances of the approaches in the differentiation of NKTLs and DLBCLs. Spearman's rank correlations were used to analyze the correlation between ADC value with the higher AUC and Ki-67 expression.

RESULTS

For conventional MRI, localization in the nasal cavity and poor or moderate enhancement indicated an NKTL, whereas localization in the paranasal sinus and intense enhancement indicated a DLBCL, with sensitivity, specificity and area under the curve(AUC)value of 88.5%, 85.0% and 0.883, respectively. A combination with a cut-off ADC value of 0.646 × 10^-3 mm²/s yielded sensitivity, specificity and AUC values of 100.0%, 80.0% and 0.951, respectively. A significant difference between the AUCs for conventional MRI and MRI in combination with DWI (p = 0.02) was identified. Ki-67 expression of NKTLs was significantly lower than that of DLBCLs (p < 0.001). Besides, there was an inversely poor correlation between them in the overall sample (r = -0.395, p < 0.001). However, the ADC value was not significantly correlated with Ki-67 LI in neither NKTLs nor DLBCLs (both p > 0.05).

CONCLUSIONS

Location and enhancement degree were the most valuable conventional MRI features for differentiating between NKTLs and DLBCLs. A combination of DWI and MRI could significantly improve the differential performance. ADC values may be used to noninvasively evaluate the proliferation level of sinonasal NHLs.

Evaluation of multiparametric MRI differentiating sinonasal angiomatous polyp from malignant tumors

PURPOSE

Sinonasal angiomatous polyps (SAPs) can be misdiagnosed as malignant tumors due to aggressive clinical behaviors. The purpose of this study was to evaluate the diagnostic accuracy of multiparametric MRI in differentiating SAPs from malignant tumors.

METHODS

This retrospective study included 31 patients with pathologically proven SAPs and 36 patients with malignant tumors in maxillary sinus and nasal cavity. All the patients underwent conventional MRI and dynamic contrast-enhanced (DCE) MRI on 3T MR scanners. Diffusion-weighted (DW) MR imaging was performed in 45 patients. All the MR images were retrospectively analyzed independently by two authors.

RESULTS

Significant differences were found in T1 homogeneity, T2 signal intensity ratio, peripheral hypointense rim on T2WI, and soft tissue infiltration between SAP and malignant tumors (P = 0.004, < 0.001, < 0.001, and = 0.001, respectively). SAPs usually show heterogeneous signal intensity on T1WI, peripheral hypointense rim on T2WI, and higher T2 signal intensity ratio. The tumor size of SAP (4.01 ± 1.08 cm) was slightly smaller than that of malignant tumors (4.56 ± 1.12 cm) (P = 0.045). There were significant differences in DCE-MRI parameters including Tpeak, CImax, WR, TIC types, and progressive enhancement (P = 0.009, < 0.001, = 0.001, = 0.001, and < 0.001, respectively) between SAPs and malignant tumors. All the 31 SAPs showed progressive enhancement on DCE-MRI, while none of the malignant tumors showed progressive enhancement (accuracy 100%). The mean ADC of SAP (1.75 ± 0.30 × 10^-3 mm²/s) was higher than that of malignant tumors (1.18 ± 0.31 × 10^-3 mm²/s) (P < 0.001).

CONCLUSION

Multiparametric MRI showed high diagnostic performance in differentiating SAPs from malignant tumors. Progressive enhancement on DCE-MRI is the most effective feature of SAP.

Application of diffusion-weighted MR imaging with ADC measurement for distinguishing between the histopathological types of sinonasal neoplasms

PURPOSE

To evaluate the potential contribution of quantitative DWI parameters including ADC_mean and ADC_ratio values to help in distinguishing the histopathological types of sinonasal neoplasms.

METHODS

This retrospective study included 83 patients (50 males, 33 females; mean age 61 years) with pathologically proven untreated sinonasal neoplasms who have undergone diffusion-weighted MRI imaging from February 2010 to August 2017. Diffusion-weighted MRI was performed on a 3 T unit with b factors of 0 and 1000 s/mm², and ADC maps were generated. Mean ADC values of sinonasal tumors and ADC ratios (ADC_mean of the tumor to ADC_mean of pterygoid muscles) were compared with the histopathological diagnosis by utilizing the Kruskal-Wallis non-parametric test.

RESULTS

Mean ADC_mean and ADC_ratio were 0.8 (SD, ±0.4) × (10^-3 mm²/s) and 1.2 (SD, ±0.5), respectively, and each parameter was significantly different between histopathological types (p < 0.05). Mean ADC_mean and ADC_ratio were higher in adenoid cystic carcinoma (ACC) than in SCC, lymphoma, neuroendocrine carcinoma and sinonasal undifferentiated carcinoma (SNUC) (p < 0.05). Optimized ADC_mean thresholds of 0.79, 0.81, 0.74 and 0.78 (10^-3 mm²/s) achieved maximal discriminatory accuracies of 100%, 79%, 100% and 89% for ACC/SNUC, ACC/SCC, ACC/neuroendocrine carcinoma, and ACC/lymphoma, respectively.

CONCLUSIONS

The optimized ADC_mean threshold of 0.80 (10^-3 mm²/s) could be used to differentiate ACC from non-ACC sinonasal neoplasms with maximal discriminatory accuracy (82%) and sensitivity of 100%. However, there is considerable overlapping of the ADC_mean and ADC_ratio values among non-ACC sinonasal neoplasms hence surgical biopsy is still needed.

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.

Imaging Characteristics of Malignant Sinonasal Tumors

Malignancies of the nasal cavity and paranasal sinuses account for 1% of all malignancies and 3% of malignancies of the upper aerodigestive tract. In the sinonasal tract, nearly half of all malignancies arise in the nasal cavity, whereas most of the remaining malignancies arise in the maxillary or ethmoid sinus. Squamous cell carcinoma is the most common histological subtype of malignant tumors occurring in this area, followed by other epithelial carcinomas, lymphomas, and malignant soft tissue tumors. Although many of these tumors present with nonspecific symptoms, each tumor exhibits characteristic imaging features. Although complex anatomy and various normal variants of the sinonasal tract cause difficulty in identifying the origin and extension of large sinonasal tumors, the invasion of vital structures such as the brain, optic nerves, and internal carotid artery affects patients’ prognosis. Thus, diagnostic imaging plays a key role in predicting the histological subtype and in evaluating a tumor extension into adjacent structures. This article describes the computed tomography and magnetic resonance imaging findings for malignant sinonasal tumors.

Technical Note: Diffusion-weighted MRI with minimal distortion in head-and-neck radiotherapy using a turbo spin echo acquisition method

PURPOSE

Diffusion-weighted (DW) MRI, showing high contrast between tumor and background tissue, is a promising technique in radiotherapy for tumor delineation. However, its use for head-and-neck patients is hampered by poor geometric accuracy in conventional echo planar imaging (EPI) DW-MRI. An alternative turbo spin echo sequence, DW-SPLICE, is implemented and demonstrated in patients.

METHODS

The DW-SPLICE sequence was implemented on a 3.0 T system and evaluated in 10 patients. The patients were scanned in treatment position, using a customized head support and immobilization mask. Image distortions were quantified at the gross tumor volume (GTV) using field map analysis. The apparent diffusion coefficient (ADC) was evaluated using an ice water phantom.

RESULTS

The DW images acquired by DW-SPLICE showed no image distortions. Field map analysis at the gross tumor volumes resulted in a median distortion of 0.2 mm for DW-SPLICE, whereas for the conventional method this was 7.2 mm. ADC values, measured using an ice water phantom were in accordance with literature values.

CONCLUSIONS

The implementation of DW-SPLICE allows for diffusion-weighted imaging of patients in treatment position with excellent geometrical accuracy. The images can be used to facilitate target volume delineation in RT treatment planning.

Differentiation of olfactory neuroblastomas from nasal squamous cell carcinomas using MR diffusion kurtosis imaging and dynamic contrast-enhanced MRI

PURPOSE

To evaluate the use of magnetic resonance (MR) diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MR imaging (DCE-MRI) in the differentiation of olfactory neuroblastomas (ONBs) from squamous cell carcinomas (SCCs).

MATERIALS AND METHODS

DKI and DCE-MRI were performed in 17 patients with ONBs and 23 patients with SCCs on a 3T MR scanner. Parameters derived from DKI and DCE-MRI were measured and compared between ONBs and SCCs using an independent samples t-test. The sensitivity, specificity, accuracy, positive predictive values (PPV), negative predictive values (NPV), and the area under the receiver operating characteristic (ROC) curve were determined.

RESULTS

The mean kurtosis (K) value of ONBs was significantly higher than that of SCCs (P < 0.001), and the mean fractional volume in the extravascular extracellular space (V_e ) value of ONBs was lower than that of SCCs (P < 0.001). The ROC curve analyses yielded a cutoff K value of 0.953, with a sensitivity of 94.1%, a specificity of 69.6%, and an accuracy of 80.0%; the cutoff V_e value was 0.493, with a sensitivity of 70.6%, a specificity of 95.7%, and an accuracy of 85.0%. A parallel test with K value >0.953 or V_e value ≤0.493 achieved a sensitivity of 94.1%, a specificity of 100.0%, and an accuracy of 97.5% for differentiating ONBs from SCCs.

CONCLUSION

The K value of DKI and V_e value of DCE-MRI have potential use in the differentiation of ONBs and SCCs.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:354-361.

Multiparametric MRI findings of sinonasal rhabdomyosarcoma in adults with comparison to carcinoma

PURPOSE

To identify magnetic resonance imaging (MRI) features of sinonasal rhabdomyosarcoma in adults, including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI features as compared with carcinomas.

MATERIALS AND METHODS

Sixty-four patients were included in this study, including 12 sinonasal rhabdomyosarcomas and 52 sinonasal carcinomas. MRI was completed in all 64 patients with a 3T MR scanner. Conventional MR (nonenhanced and static contrast-enhanced) imaging features, DCE-MRI parameters, and the apparent diffusion coefficients (ADCs) were analyzed by two authors independently (X.Y.W. and Y.Z.W.).

RESULTS

Compared with gray matter, sinonasal rhabdomyosarcomas appeared isointense on T₁ -weighted images in 11 cases (91.7%, 11 of 12), and hyperintense on T₂ -weighted images in 9 patients (75%, 9 of 12). After contrast, sinonasal rhabdomyosarcomas showed inhomogeneous enhancement in 10 cases (83.3%, 10 of 12). Skull involvement was found in eight patients (66.7%) with rhabdomyosarcomas. On T₂ -weighted images, sinonasal carcinomas demonstrated isointense in 31 cases (59.6%, 31/52), hyperintense in 14 (26.9%, 14/52), and hypointense in 7 (13.5%, 7/52). Skull involvement was detected in 14 cases (14/52, 26.9%). There were significant differences in T₂ signal intensity (P = 0.005) and skull involvement (P = 0.016) between sinonasal rhabdomyosarcoma and carcinomas. There was a marginal difference in time to peak enhancement (P = 0.061), while no difference in time to maximum enhancement (P = 0.403), maximum contrast index (P = 0.368), and time-intensity curve types (P = 0.138) between rhabdomyosarcoma and carcinomas. The ADCs of sinonasal rhabdomyosarcoma were significantly lower than those of sinonasal carcinomas (P < 0.001).

CONCLUSION

A multiparametric approach using conventional MRI with added ADCs had the potential to improve the diagnostic accuracy of sinonasal rhabdomyosarcoma in adults. Evidence level: 4 J. Magn. Reson. Imaging 2017;45:998-1004.

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.