Sinonasal Neoplasms

Sinonasal Tumors: What the Multidisciplinary Cancer Care Board Wants to Know

Sinonasal neoplasms are a remarkably heterogeneous group, reflecting the numerous tissue types present in the nasal cavity and paranasal sinuses. These entities can be relatively benign (ie, respiratory epithelial adenomatoid hamartoma) or can be exceedingly aggressive (ie, NUT carcinoma). Certain sinonasal tumors have a propensity to spread through local invasion and destruction, while others have a high likelihood of perineural spread. The genetic and molecular mechanisms underlying sinonasal tumor behavior have recently become better understood, and new tumor types have been described using these genetic and molecular data. This has prompted an expansion in the number of tumors included in the World Health Organization fifth edition classification system for head and neck tumors, along with a new classification structure. Radiologists' familiarity with this classification structure is crucial to understanding the expected behavior of these tumors and to collaboration with the multidisciplinary cancer care board in making decisions for optimal patient care. ©RSNA, 2024.

Imaging of the Sinonasal Cavities

This article describes the various abnormalities that affect the sinonasal cavities and discusses inflammations, tumors, and tumor-like conditions. Specific imaging evaluations that focus on the sinonasal cavities are described in more detail.

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.

Multi-parametric arterial spin labeling and diffusion-weighted imaging of paranasal sinuses masses

PURPOSE

To evaluate arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) in discrimination of benign from malignant paranasal sinus (PNS) tumors.

MATERIAL AND METHODS

A prospective study was done upon 42 cases of PNS masses that underwent magnetic resonance ASL and DWI of the head. Tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the masses were calculated by two observers. The pathological diagnosis was malignant (n = 28) and benign (n = 14) cases.

RESULTS

For both observers, the malignant PNS masses had significantly higher TBF (P < 0.001, 0.001) and lower ADC (P < 0.001, 0.001) than in benign masses. The ROC curve analysis of TBF, The threshed TBF was (121.45, 122.68 mL/100 g/min) used for differentiation between benign and malignant PNS masses, revealed sensitivity (92.9%, 89.3%), specificity (85.7%, 85.7%), accuracy (90.5%, 88.1%) and the AUC was 0.87 and 0.86 by both observers. the ROC curve analysis of ADC, The threshold ADC (1.215, 1.205 X10^-3mm2/s) was used for differentiation between benign and malignant PNS masses, revealed sensitivity (96.4%, 89.3%), specificity (78.6%, 78.6%), accuracy of (90.5%, 85.7%) and the AUC was 0.93 and 0.92 by both observers. Combined analysis of TBF and ADC used for differentiation between benign and malignant PNS masses had revealed sensitivity (96.4%, 89.3%), specificity (92.9%, 85.7%) accuracy of (95.2%, 88.1%) and AUC. (0.995, 0.985) for both observers.

CONCLUSION

Combined using of TBF and ADC have a role in differentiation malignant from benign PNS masses.

Texture analysis of diffusion kurtosis imaging for differentiating malignant from benign sinonasal lesions: added value to conventional imaging features

OBJECTIVES

To evaluate the performance of texture analysis (TA) of diffusion kurtosis imaging (DKI) in differentiating malignant from benign sinonasal lesions, and its added value to the conventional imaging features.

METHODS

Fifty-eight patients with malignant and 40 patients with benign sinonasal lesions were retrospectively enrolled. Conventional CT and MRI features were reviewed. Texture parameters were obtained and compared between two groups. Multivariate logistic regression analysis was used to identify the most valuable variables. Receiver operating characteristic curves were performed to assess the differentiating performance of independent variables and their combination.

RESULTS

There were significant differences in tumor necrosis, bone erosion and soft tissue invasion between the two groups (all p < 0.05). There were significant differences in the 10th and entropy of Apparent diffusion coefficient map, the mean, 10th and entropy of D map, the mean and 90th of K map between the two groups (all p < 0.002). The bone erosion, entropy of D, and mean of K were independent variables associated with malignant tumors. Receiver operating characteristic analyses indicated that the combination of three features possessed better differentiating performance than bone erosion alone (p = 0.003).

CONCLUSION

TA of DKI could supply incremental value to conventional imaging features for pre-operative differential diagnosis between benign and malignant sinonasal lesions.

ADVANCES IN KNOWLEDGE

The present study is the first to combine conventional imaging features and the TA of DKI in the differential diagnosis between benign and malignant sinonasal lesions. Our findings suggest that TA of DKI could supply incremental value to conventional imaging features.

Sinonasal Diseases Affecting the Orbit - A Radiological Pattern-Based Approach

The orbit is surrounded on three sides by paranasal sinuses with only a thin rim of bone separating the 2 at each site, predisposing it to the risk of being affected by the diseases of primary sinonasal compartment. A wide range of sinonasal pathologies can show orbital involvement and may present mainly with orbital symptoms. While most of these are due to contiguous involvement, a few others may be caused by systemic diseases that often involve both, the orbit and the sinuses in a noncontiguous manner. In this article, we have classified these diseases based on their radiological appearances into 5 patterns: Pattern 1 - Fat stranding predominant, Pattern 2 - Soft tissue without bone destruction, Pattern 3 - Soft tissue with bone destruction, Pattern 4 - Bony pathologies, Pattern 5 - Sinus volume changes. Various pathologies have been classified into these patterns considering the most typical changes in each of the disease processes. We briefly review these patterns, their hallmark radiological signs, typical examples of each pattern as well as review the various diseases process highlighting their radiological appearances. Imaging plays a crucial role in identifying the epicentre of the disease process, narrowing down the differential diagnoses, identifying management modifying complications, planning the surgical management as well as in the follow up of several such lesions. This unique radiological approach, although not absolute, aims to provide a new insight and working algorithm to help narrowing down the differential diagnoses.

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.