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

Abstract

Objective: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. Methods: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists’ diagnosis, and radiomic models were evaluated. Results: Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T2 weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma. Conclusion: MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. Advances in knowledge: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T2 weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

“…It can also contribute to predict the prognosis in some instances [15][16][17]. Several recent articles have explored the value of radiomics, clinical, and their combined models for the classification of PA, WT, and malignancy [18][19][20][21][22]. However, the selection of clinical factors is not uniform.…”

“…Compared with T1-weighted images (T1WIs), T2WIs can provide more details about the internal structure of the tumor [23][24][25]. Furthermore, the T2WI-based radiomics model has the best diagnostic performance to differentiate WT from PA [22].…”

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

“…Radiomics is an emerging method that could improve diagnostic accuracy through the quantitative analysis of data from images, and offers advantages over conventional biopsy including noninvasiveness, virtually unlimited repeatability, and possibility to assess the whole tumor tissue and to perform longitudinal follow-up testing [25] , [26] , [27] , [28] . While several articles have been published trying to differentiate benign from malignant salivary gland tumors based on radiomics features extracted from head-and-neck MRI examinations, there is still no consensus on which image sequences should be analyzed, with some studies using T2w images [11] and other ones DWI [15] or a combination of sequences (such as post-contrast T1w, T2w, DCE or apparent diffusion coefficient [ADC] images) [13] , [14] , [17] , [29] . In our study we selected to analyze T2w and pcfsT1w images, because these sequences are an essential part of MRI examinations aimed to the evaluation of a parotid mass, providing key information for the differential diagnosis.…”

“…Fruehwald-Pallamar et al [13] and Sarioglu et al [12] found that texture analysis features derived from pcfsT1w images provided the most significant textural information in the discrimination between PA and WT. Conversely, Vernuccio et al [29] demonstrated that an MRI-based predictive radiomics model based on texture analysis of T2w images improved the diagnostic performance of non-subspecialized radiologists for the differential diagnosis between PA and WT. Our findings revealed that texture features obtained from T2w and pcfsT1w images can have good discriminatory performance, with skewness derived from T2-weighted images yielding highest specificity.…”

Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors

“…ADC-based texture analysis, as a non-invasive and quantitative additional supporting tool, can extract features of entire tumors and go beyond individual-based visual assessment ( 34 ). Previously, many studies ( 26 , 34 – 42 ) have explored the computer-assisted discrimination of benign and malignant parotid gland tumors, but only a few studies have evaluated the role of ADC-based radiomics features in the differentiation of parotid lesions ( 29 , 31 , 34 ). In our study, the ADC-based radiomics features were from three different manufacturers, which still shows a good performance in differentiating PA and WT from MTs.…”