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

Apparent diffusion coefficient for differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck: a systematic review and meta-analysis

BACKGROUND

Radiological differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck is often difficult due to their similarities.

PURPOSE

To evaluate the diagnostic benefit of apparent diffusion coefficient (ADC) calculated from diffusion-weighted imaging (DWI) in differentiating the two.

MATERIAL AND METHODS

A systematic review was performed by searching the MEDLINE, Scopus, and Embase databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Forest plots and the pooled mean difference of ADC values were calculated to describe the relationship between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Heterogeneity among studies was evaluated using the Cochrane Q test and I2 statistic.

RESULTS

The review identified eight studies with 440 patients (441 lesions) eligible for meta-analysis. Among all studies, the mean ADC values of squamous cell carcinoma was 0.88 × 10-3mm2/s and that of lymphoma was 0.64 × 10-3mm2/s. In the meta-analysis, the ADC value of lymphoma was significantly lower than that of squamous cell carcinoma (pooled mean difference = 0.235, 95% confidence interval [CI] = 0.168-0.302, P <0.0001). The Cochrane Q test (chi-square = 55.7, P <0.0001) and I2 statistic (I2 = 87.4%, 95% CI = 77.4-93.0%) revealed significant heterogeneity.

CONCLUSION

This study highlights the value of quantitative assessment of ADC for objective and reliable differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Conclusions should be interpreted with caution due to heterogeneity in the study data.

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.

Computed Tomography and Magnetic Resonance Imaging Findings Contribute to Differentiating Solid- and Nonsolid-Type Adenoid Cystic Carcinoma in Maxillary Sinus

PURPOSE

This study aimed to evaluate the imaging features of maxillary sinus adenoid cystic carcinoma (ACC) on computed tomography (CT) and magnetic resonance imaging (MRI) and to investigate the imaging differences between solid and nonsolid maxillary sinus ACC.

METHODS

We retrospectively reviewed 40 cases of histopathologically confirmed ACC of the maxillary sinus. All the patients underwent CT and MRI. Based on the histopathological characteristics, the patients were classified into 2 groups: ( a ) solid maxillary sinus ACC (n = 16) and ( b ) nonsolid maxillary sinus ACC (n = 24). Imaging features such as tumor size, morphology, internal structure, margin, type of bone destruction, signal intensity, enhancement changes, and perineural tumor spread on CT and MRI, were evaluated. The apparent diffusion coefficient (ADC) was measured. Comparisons of imaging features and ADC values were performed between the solid and nonsolid maxillary sinus ACC using χ 2 and nonparametric tests.

RESULTS

The internal structure, margin, type of bone destruction, and degree of enhancement significantly differed between solid and nonsolid maxillary sinus ACC (all P < 0.05). The ADC of the solid maxillary sinus ACC was considerably lower than that of the nonsolid maxillary sinus ( P < 0.05).

CONCLUSIONS

Computed tomography and MRI may aid in the differentiation of solid and nonsolid types of maxillary sinus ACC.

Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma

OBJECTIVES

To develop and validate a nomogram based on whole-tumour histograms of apparent diffusion coefficient (ADC) maps for predicting malignant transformation (MT) in sinonasal inverted papilloma (IP).

METHODS

This retrospective study included 209 sinonasal IPs with and without MT, which were assigned into a primary cohort (n = 140) and a validation cohort (n = 69). Eight ADC histogram features were extracted from the whole-tumour region of interest. Morphological MRI features and ADC histogram parameters were compared between the two groups (with and without MT). Stepwise logistic regression was used to identify independent predictors and to construct models. The predictive performances of variables and models were assessed using the area under the curve (AUC). The optimal model was presented as a nomogram, and its calibration was assessed.

RESULTS

Four morphological features and seven ADC histogram parameters showed significant differences between the two groups in both cohorts (all p < 0.05). Maximum diameter, loss of convoluted cerebriform pattern, ADC10th and ADCSkewness were identified as independent predictors to construct the nomogram. The nomogram showed significantly better performance than the morphological model in both the primary (AUC, 0.96 vs 0.88; p = 0.006) and validation (AUC, 0.96 vs 0.88; p = 0.015) cohorts. The nomogram showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram is clinically useful.

CONCLUSIONS

The developed nomogram, which incorporates morphological MRI features and ADC histogram parameters, can be conveniently used to facilitate the pre-operative prediction of MT in IPs.

Accuracy of magnetic resonance imaging in the assessment of depth of invasion in tongue carcinoma: A systematic review and meta-analysis

Tongue carcinoma constitutes 10.4-46.9% of all oral squamous cell carcinomas (OSCCs) and is notoriously known for invading tissues deeper than the evident gross margins. The deeper the tumor invades, the higher are its chances of future morbidity and mortality due to extensive neck dissection and risk of recurrence. Magnetic resonance imaging (MRI) is a noninvasive diagnostic aid used for measuring a preoperative tumor's depth of invasion (DOI) as it can efficiently outline soft tissue tumors from adjacent normal tissue. To assess various MRI modalities used in measuring DOI in tongue carcinoma and their reliability compared with other DOI measuring modalities. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42022330866), and the following Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) Diagnostic Test Accuracy guidelines were performed. PubMed electronic database was searched using a combination of keywords for relevant articles in the English language since 2016. Critical appraisal was carried out using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) risk-of-bias (RoB) assessment tool. A weighted mean difference (WMD) was calculated between MRI and histopathological DOI along with pooled correlation and subgroup analysis, where possible. A total of 795 records were retrieved of which 17 were included in the final review with 13 included for meta-analysis. A high RoB was found for most studies for all parameters except flow and timing. WMD showed a statistically significant MRI overestimation of 1.90 mm compared with histopathology. Subgroup analysis showed the 1.5 Tesla machine to be superior to the 3.0 Tesla machine, while imaging sequence subgroup analysis could not be performed. MRI is a viable preoperative DOI measurement modality that can help in efficient treatment planning to decrease surgical morbidity and mortality.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study

PURPOSE

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC.

METHODS

One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed.

RESULTS

The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05).

CONCLUSION

ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

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.

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.

Characteristic multimodal imaging of palatal follicular lymphoma: a case report on effectiveness of CT, diffusion-weighted MR imaging and intraoral ultrasonography

Oral lymphomas are relatively uncommon. Follicular lymphoma is the second most common subtype of non-Hodgkin lymphoma. We report characteristic multimodal imaging of palatal follicular lymphoma, especially CT, diffusion-weighted MR imaging (DWI) and intraoral ultrasonography. A 67-year-old woman presented with swelling on the right side of the palate within 2 months. On clinical examination, an approximately 35 × 20 mm mass lesion with elastic soft was found to overlay the right side of the palate. Contrast-enhanced CT image showed a mass with homogeneous enhancement on the right side of the palate, and bone tissue algorithm CT showed focal erosion of the right posterior maxilla. Regarding MR imaging, on T1-weighted image, the mass showed low signal intensity and homogeneous enhancement, and T2-weighted and STIR images revealed intermediate and high signal intensity, respectively. Furthermore, DWI and apparent diffusion coefficient (ADC) map showed high and low signal intensity, respectively. ADC value of the mass was 0.60 × 10^-3 mm²s^-1. On intraoral ultrasonography, the mass showed clear boundary, hypoechoic echogenicity, homogeneous internal architecture, vascular signals using color Doppler imaging and heterogeneous hard using strain elastography. A partial biopsy of the palatal region was performed. Histopathological diagnosis was follicular lymphoma. This case suggests that multimodal imaging, especially CT, DWI with ADC map and intraoral ultrasonography with color Doppler imaging and strain elastography, could be effective for evaluating palatal lesions.

Texture analysis of conventional magnetic resonance imaging and diffusion-weighted imaging for distinguishing sinonasal non-Hodgkin's lymphoma from squamous cell carcinoma

PURPOSE

To evaluate the value of texture analysis (TA) of conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differential diagnosis between sinonasal non-Hodgkin's lymphoma (NHL) and squamous cell carcinoma (SCC).

METHODS

Forty-two patients with sinonasal SCC and 30 patients with NHL were retrospectively enrolled. TAs were performed on T2-weighted image (T2WI), apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted image (T1WI). Texture parameters, including mean value, skewness, kurtosis, entropy and uniformity were obtained and compared between sinonasal SCC and NHL groups. Receiver-operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic value and identify the independent TA parameters.

RESULTS

The mean value and entropy of ADC, and mean value of contrast-enhanced T1WI were significantly lower in the sinonasal NHL group than those in the SCC group (all P < 0.05). ROC analysis indicated that the entropy of ADC had the best diagnostic performance (AUC 0.832; Sensitivity 0.95; Specificity 0.67; Cutoff value 6.522). Logistic regression analysis showed that the entropy of ADC (P = 0.002, OR = 26.990) was the independent parameter for differentiating sinonasal NHL from SCC.

CONCLUSION

TA parameters of conventional MRI and DWI, particularly the entropy value of ADC, might be useful in the differentiating diagnosis between sinonasal NHL and SCC.

Radiological features of human papillomavirus-related multiphenotypic sinonasal carcinoma: systematic review and case series

PURPOSE

To comprehensively summarize the radiological characteristics of human papillomavirus (HPV)-related multiphenotypic sinonasal carcinomas (HMSCs).

METHODS

We reviewed the findings for patients with HMSCs who underwent computed tomography (CT) and/or magnetic resonance imaging (MRI) and included nine cases from nine publications that were identified through a systematic review and three cases from our institution. Two board-certified radiologists reviewed and evaluated the radiological images.

RESULTS

The locations in almost all cases included the nasal cavity (11/12, 91.7%). The involved paranasal sinuses included the ethmoid sinus (6/12, 50.0%) and maxillary sinus (3/12, 25.0%). The mean long diameter of the tumors was 46.3 mm. The margins in 91.7% (11/12) of the cases were well-defined and smooth. Heterogeneous enhancement on contrast-enhanced CT, heterogeneous high signal intensities on T2-weighted images and heterogeneous enhancement on gadolinium-enhanced T1-weighted images were noted in 2/2, 5/5, and 8/8 cases, respectively. Mean apparent diffusion coefficient values in two cases of our institution were 1.17 and 1.09 × 10^-3 mm²/s. Compressive changes in the surrounding structures were common (75%, 9/12). Few cases showed intraorbital or intracranial extension. None of the cases showed a perineural spread, neck lymph node metastasis, or remote lesions.

CONCLUSIONS

We summarized the CT and MRI findings of HMSCs. Knowledge of such characteristics is expected to facilitate prompt diagnosis and appropriate management.

A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study

Purpose

To develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively predict the risk of early relapse (ER) in advanced sinonasal squamous cell carcinomas (SNSCCs).

Methods

A total of 152 SNSCC patients (clinical stage III-IV) who underwent diffusion-weighted imaging (DWI) were included in this study. The training cohort included 106 patients assessed at the headquarters of our hospital using MR scanner 1. The testing cohort included 46 patients assessed at the branch of our hospital using MR scanner 2. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). Furthermore, the patients were classified into high- or low-risk ER subgroups according to the optimal cutoff value of the nomogram using X-tile. The recurrence-free survival probability (RFS) of each subgroup was assessed.

Results

ER was noted in 69 patients. The radscore included 8 selected radiomic features. The radscore, T stage and surgical margin were independent predictors. The nomogram showed better performance (AUC = 0.92) than either the radscore or the clinical factors in the training cohort (P < 0.050). In the testing cohort, the nomogram showed better performance (AUC = 0.92) than the clinical factors (P = 0.016) and tended to show better performance than the radscore (P = 0.177). The nomogram demonstrated good calibration and clinical utility. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (P < 0.001).

Conclusions

The ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs.

Current opinions on diagnosis and treatment of adenoid cystic carcinoma

Adenoid cystic carcinoma (ACC) is a rare malignant tumor derived mainly from the salivary glands, representing approximately 1% of all headandneck carcinomasand 10% of all salivary gland neoplasms. ACC displays a paradoxical behavioral combination of an indolent growth pattern but an aggressive progression, with local recurrence and distant metastasis. The propensity of ACC of the head and neck (ACCHN) for perineural invasion and its anatomical location, especially if it extends to the nasal cavity and paranasal sinuses, facilitates tumor involvement in the surrounding structures, such as the orbit, pterygopalatine fossa, Meckel'scave, and cavernous sinus, which can lead to skull base involvement and intracranial extension. Despite advances in molecular mechanisms and diagnostic imaging, ACC treatment remainschallenging due to the lack ofconsensuson treatment patterns. In this review, we aimed toprovideanupdatedinsight intothe understanding of ACCHN by focusing on clinical behavior, imaging diagnosis, pathological features, and therapeutic strategies. We reviewed the molecular mechanisms, especially in ACCHN with perineural invasion, and elaborated on treatment options, including chemotherapy, targeted therapies, and immunotherapy, to establish a comprehensive understanding of ACC to arrive at a policy for proper diagnosis, preoperative evaluation, and therapeutic strategies.

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.

MR imaging of Nasal and Paranasal Sinus Malignant Neoplasms

MRI is useful for evaluating sinonasal malignancies. In particular, MRI can provide important information pertinent to treatment planning, such as delineating the presence of intracranial and orbital extension. This article reviews the MRI protocols, staging, imaging features, and differential diagnosis related to malignant nasal and paranasal sinus neoplasms.

BLADE turbo gradient- and spin-echo in the assessment of sinonasal lesions: a comprehensive comparison of image quality in readout-segmented echo-planar imaging

BACKGROUND

A two-dimensional turbo gradient-echo and spin-echo diffusion-weighted pulse sequence with a non-Cartesian BLADE trajectory (TGSE BLADE) can eliminate image artifacts and distortion with clinically acceptable scan times. This process has the potential to overcome the shortcomings of current diffusion-weighted imaging (DWI) techniques, especially in the sinonasal region.

PURPOSE

To investigate the feasibility of TGSE BLADE in the assessment of sinonasal lesions and compare the quality of TGSE BLADE with RESOLVE images both qualitatively and quantitatively.

MATERIAL AND METHODS

A total of 36 patients with sinonasal lesions were included in this prospective study. DW images acquired using TGSE BLADE and RESOLVE were performed with the same acquisition time. Two independent observers evaluated the qualitative parameters (overall image quality, lesion visibility, and geometric distortion) and quantitative parameters (geometric distortion ratio [GDR], signal-to-noise ratio [SNR], contrast, contrast-to-noise ratio [CNR], and apparent diffusion coefficient [ADC] value) of the two sequences.

RESULTS

Qualitative assessment revealed that TGSE BLADE exhibited higher overall image quality (P < 0.001) and lesion visibility (P < 0.001) and less geometric distortion (P < 0.001) than RESOLVE. Quantitative assessment showed that TGSE BLADE images exhibited higher contrast (P < 0.001) and CNR (P < 0.001) and lower GDR (P < 0.05) and SNR (P < 0.001) than RESOLVE images. The ADC value of TGSE BLADE was significantly lower than that of RESOLVE (P < 0.05).

CONCLUSION

TGSE BLADE can reduce susceptibility artifacts and geometric distortion more than RESOLVE and appears to be a promising diffusion imaging sequence for the assessment of sinonasal lesions.

Sinonasal Squamous Cell Carcinoma, a Narrative Reappraisal of the Current Evidence

Simple Summary

Sinonasal squamous cell carcinomas are a group of diverse tumors affecting the nasal cavity and paranasal sinuses. As a direct consequence of their rarity and heterogeneity, diagnosis is challenging, and treatment does not follow universally accepted protocols. Though surgery represents the mainstay of treatment, neoadjuvant and adjuvant therapies have pivotal roles in improving outcomes of patients treated with curative intent. Indications to endoscopic surgery have been expanding over the last three decades, but a considerable number of patients affected by sinonasal squamous cell carcinoma still need open surgical procedures. Management of the neck in patients affected by sinonasal squamous cell carcinoma is controversial. Curative-intended treatment of recurrent and/or metastatic tumors, alongside palliation of uncurable cases, represent poorly explored aspects of this disease.

Abstract

Sinonasal squamous cell carcinoma is a rare tumor affecting the nasal cavity and paranasal sinuses. Several aspects of this disease, ranging from epidemiology to biology, pathology, diagnosis, staging, treatment, and post-treatment surveillance are controversial, and consensus on how to manage this sinonasal cancer is lacking. A narrative literature review was performed to summarize the current evidence and provide the reader with available data supporting the decision-making process in patients affected by sinonasal squamous cell carcinoma, alongside the authors’ personal opinion on the unsolved issues of this tumor. The review has highlighted several advances in molecular definition of epithelial cancers of the sinonasal tract. Surgery represents the pivot of treatment and is performed through an endoscopic transnasal approach whenever feasible. Open surgery is required for a large proportion of cases. Reconstruction of the defect follows principles of skull base and cranio-maxillo-facial reconstruction. Chemotherapy is given as neoadjuvant treatment or concomitantly to radiotherapy. Photon-based radiation therapy has a crucial role in the adjuvant setting. Particle therapy is providing promising results. Management of the neck should be planned based on the presence of clinically appreciable metastases, primary tumor extension, and need for recipient vessels. Biotherapy and immunotherapy are still underexplored therapeutical modalities.

MRI of Sinonasal Malignancies

ABSTRACT

Sinonasal cavity is an important subsite in head and neck tumors. There are a myriad of malignancies that present within this area. Adequate staging for treatment planning requires multimodality evaluation. Magnetic resonance imaging (MRI) forms an important component in the evaluation of sinonasal tumors. We sought to review the most common sinonasal tumors, including sinonasal anatomy, clinical features, and common imaging features. A literature review was performed to evaluate common sinonasal tumors. Owing to the different tissue types within the sinonasal cavity, there are multiple different tumor pathologies within the sinonasal compartment. Most present in adults although some present in the young. Many of these tumor types have imaging overlaps, although some have a characteristic appearance. MRI can aid in soft tissue delineation, evaluation of multicompartmental extension, intracranial spread, and perineural spread. Sinonasal tumors are a heterogeneous group for which soft tissue delineation via MRI forms an important role in ensuring adequate treatment planning to improve outcomes, decreasing morbidity, and improve functional outcomes.

MRI features of different types of sinonasan rhabdomyosarcomas: a series of eleven cases

OBJECTIVE

To retrospectively analyze magnetic resonance imaging (MRI) features of various pathological subtypes of sinonasal rhabdomyosarcoma (RMS) and explore correlations between imaging features and pathological subtypes.

METHODS

In total, 11 cases with embryonal, alveolar or pleomorphic sinonasal RMSs, confirmed by surgical pathology, were selected. Their characteristics and distinctive imaging features were analysed, and the correlation between pathology and imaging features was explored.

RESULTS

Bone destruction was observed in all 11 cases with RMS. Expansive growth was predominant in three alveolar and three embryonal RMS cases, and creeping growth was predominant in two alveolar, two embryonal and one pleomorphic RMS cases. Signs of residual mucosa were observed in all 11 cases, and 10 cases showed involvement of multiple sinus cavities and orbital cavities. All cases exhibited mild-to-intermediate enhancement.

CONCLUSION

Sinonasal RMSs have the following characteristic MRI features: ethmoid sinuses and middle nasal conchae are the prevalent sites; lesions are mainly of mild enhancement; tumours exhibit signs of residual mucosa, mild-to-intermediate enhancement and frequent orbital involvement; bone invasion and bone destruction are frequently observed; and haematogenous metastasis is not as common as lymphatic metastasis. RMSs of various pathological subtypes were not significantly distinct by imaging.

Small Cell Neuroendocrine Carcinoma of Paranasal Sinuses: Radiologic Features in 14 Cases

The purpose of this study was to explore the characteristic computed tomography (CT) and magnetic resonance (MR) features of small cell neuroendocrine carcinoma (SNEC) of paranasal sinuses.

MRI Predictors of Malignant Transformation in Patients with Inverted Papilloma: A Decision Tree Analysis Using Conventional Imaging Features and Histogram Analysis of Apparent Diffusion Coefficients

OBJECTIVE

Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis.

MATERIALS AND METHODS

In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error.

RESULTS

Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65-95%), 100% (41 out of 41; 95% CI, 91-100%), 95% (59 out of 61; 95% CI, 87-98%), and 0.966 (95% CI, 0.912-1.000), respectively.

CONCLUSION

Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

Effective combination of 3 imaging modalities in differentiating between malignant and benign palatal lesions

OBJECTIVE

The aim of this study was to evaluate whether computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) can effectively differentiate between malignant and benign palatal lesions.

STUDY DESIGN

In total, 59 patients with palatal lesions (32 malignant and 27 benign), who underwent CT, MRI, and/or PET/CT imaging examinations and had histopathological diagnoses, were divided into an analysis group (n = 46) and a validation group (n = 13). Bone changes adjacent to the lesion, MRI signal intensity, apparent diffusion coefficient (ADC), time to peak enhancement (T_peak), and maximum standardized uptake values (SUV_max) were evaluated in the analysis group. Diagnostic performance was individually assessed for each parameter for differentiating between malignant and benign lesions. A diagnostic decision tree was constructed by using useful parameters and its accuracy tested in the validation group.

RESULTS

The frequency distribution of bone change types and T_peak differed significantly between malignant and benign lesions. The ADC of malignant lymphoma was significantly lower than that of other lesions. The other parameters did not distinguish between lesion types. The accuracy of the decision tree, constructed by using bone change types, ADC, and T_peak, was 87.5%.

CONCLUSIONS

Bone change types, ADC values, and T_peak are useful for differentiating between malignant and benign palatal lesions.

Clinical utility of apparent diffusion coefficient and diffusion-weighted magnetic resonance imaging for resectability assessment of head and neck tumors with skull base invasion

BACKGROUND

The usefulness of apparent diffusion coefficient (ADC) and diffusion-weighted magnetic resonance imaging (DWI) in the detection of malignant tumors has been reported. The purpose of this study is to clarify the role of ADC and DWI for diagnosis of skull base tumors.

METHODS

A total of 27 patients with head and neck tumors with skull base invasions undergoing skull base surgery were enrolled in this study. Pathological findings of dural invasion and bone invasion were compared with the diagnostic imaging.

RESULTS

Advanced magnetic resonance imaging techniques revealed that ADC values in regions of pathological bone and dural invasions were significantly lower than in regions of no invasion. The area under the curve of ADC in bone invasions and dural invasions were 0.957 and 0.894, respectively.

CONCLUSIONS

Our findings indicate that ADC and DWI are useful tools for the diagnosis of head and neck tumors with skull base invasion.

Clinical management of emerging sinonasal malignancies

Several emerging sinonasal malignancies have recently been described in the pathology literature. Although not all distinctly classified by the World Health Organization, these rare tumors present a management challenge to surgeons and oncologists. While prior studies have summarized histologic details, a clinically focused review is currently lacking in the literature. This review describes the presentation, histopathology, imaging, treatment, and prognosis of newly described or recently evolving sinonasal malignancies while highlighting the distinguishing features of these entities. It includes teratocarcinosarcoma, human papillomavirus-related multiphenotypic carcinoma, biphenotypic sinonasal sarcoma, sinonasal renal cell-like adenocarcinoma, NUT-midline carcinoma, squamous cell carcinoma associated with inverted papilloma, sinonasal undifferentiated carcinoma, and INI-1-deficient sinonasal carcinoma. By describing the diagnosis, treatment, and prognosis of these recently defined entities, this clinical review aims to help guide oncologists in the clinical management of these patients.

Quantitative Analysis of DCE-MRI and RESOLVE-DWI for Differentiating Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoid Hyperplasia

To explore the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) analysis and readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) to distinguish nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH). Twenty-five patients with NPC and 30 patients with NPLH were evaluated. Three quantitative DCE-MRI parameters (Ktrans, Kep and Ve) and the apparent diffusion coeffcient (ADC) of lesions were calculated. The two independent samples t test or Mann-Whitney U test was used to compare the parameters between NPC and NPLH group. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability for distinguishing NPC from NPLH. A P value less than 0.05 was considered statistically significant. The difference in Ktrans value between the NPC group and the NPLH group was statistically significant, and the value of the NPC group was larger than that of the NPLH group. There was no statistical difference in Kep and Ve between the two groups. The ADC value of NPC group was smaller than that of NPLH group, and the difference was statistically significant. ROC curve analysis showed that both Ktrans and ADC were effective in diagnosing NPC and the area under the curve (AUC) was 0.773 and 0.704, respectively. In addition, the combination of Ktrans and ADC demonstrated the obviously improved AUC of 0.884. DCE-MRI and RESOLVE-DWI are effective in differentiating NPC from NPLH, especially the combination of the two models.

CT and MRI Findings of Glomangiopericytoma in the Head and Neck: Case Series Study and Systematic Review

Glomangiopericytoma is a rare sinonasal mesenchymal tumor of borderline or low malignant potential. We reviewed the CT and MR imaging findings of head and neck glomangiopericytoma via a retrospective case series study and systematic review. Our study revealed that glomangiopericytoma is a well-defined lobulated avidly enhancing soft-tissue mass with erosive bony remodeling that is most commonly found in the sinonasal cavity. Typically, it is hyperintense on T2-weighted images with vascular signal voids, has a high mean ADC value, and a wash-in and washout pattern on dynamic contrast-enhanced MR imaging. Although the CT findings are nonspecific, typical MR imaging findings, including those on the ADC map and dynamic contrast-enhanced MR imaging, may be helpful for differentiating glomangiopericytomas from other hypervascular tumors in the head and neck.

Positron emission computed tomography and magnetic resonance imaging features of sinonasal small round blue cell tumors

AIM

The sinonasal tract hosts numerous types of undifferentiated neoplasms, having small round cell morphology. The aim of this study was to determine whether sinonasal small round blue cell tumors (SRBCT) have distinct imaging features on computed tomography (CT), magnetic resonance imaging (MRI), and 18-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET)/CT.

METHODS

Seventy-three patients (43 male; M_age = 61.2 years) with histopathologically proven sinonasal SRBCT were retrospectively reviewed. Imaging features of SRBCTs including location, maximum dimension, margin characteristics, presence of calcification, sclerotic bone changes, intratumoral necrosis, tumor extension, bone destruction, bone remodeling, perineural spread, T1- and T2-weighted MRI signal intensity, qualitative features on diffusion-weighted imaging and ¹⁸F-FDG PET/CT, and pattern of contrast enhancement were analyzed using Fisher's exact test or the chi-square test. The maximum standardized uptake values (SUV_max) and apparent diffusion coefficient (ADC_mean) values of SRBCT were compared by utilizing the Kruskal-Wallis test.

RESULTS

There was a significant difference between SRBCT type regarding the tumor location (p = 0.006), ¹⁸F-FDG uptake pattern (p = 0.006), involvement of the orbit (p = 0.016) and pterygopalatine fossa (p = 0.043), the presence of perineural spread (p < 0.001), bone destruction (p = 0.034), and intratumoral necrosis (p = 0.022). Bone destruction and necrosis were more common in rhabdomyosarcoma. Perineural spread was common in sinonasal adenoid cystic carcinoma (ACC). Qualitative ¹⁸F-FDG uptake features as well as tumor location were significantly different between sinonasal ACC and sinonasal undifferentiated carcinoma. The ADC_mean and SUV_max values were not statistically different between SRBCT types.

CONCLUSIONS

Sinonasal SRBCTs have numerous distinct imaging features on CT, MRI, and ¹⁸F-FDG PET/CT that could be useful in the differentiation between lesions when the histopathologic diagnosis is inconclusive.