Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

Nardi, Cosimo; Tomei, Maddalena; Pietragalla, Michele; Calistri, Linda; Landini, Nicholas; Bonomo, Pierluigi; Mannelli, Giuditta; Mungai, Francesco; Bonasera, Luigi; Colagrande, Stefano

doi:10.1016/j.ejrad.2021.109529

Cited by 18 publications

(14 citation statements)

References 36 publications

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“…Using ADC-based radiomics analysis to detect parotid gland tumors has increasingly shown its value on different histopathological entities ( 30 – 33 ). 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 ).…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Wen

Zhang²,

Zhu³

et al. 2022

Front. Oncol.

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PurposeThe magnetic resonance imaging (MRI) findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.MethodsThis study examined diffusion-weighted imaging (DWI) obtained with echo planar imaging sequences. Eighty-eight benign tumors (BTs) [54 pleomorphic adenomas (PAs) and 34 Warthin tumors (WTs)] and 42 MTs of the parotid gland were enrolled. Each case was randomly divided into training and testing cohorts at a ratio of 7:3 and then was compared with each other, respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The region of interest (ROI) was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training dataset. Then, we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then, recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC.ResultsThe LDA model based on 13, 8, 3, and 1 features can get the highest area under the ROC curve (AUC) in differentiating BT from MT, PA from WT, PA from MT, and WT from MT on the validation dataset, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%, and 0.5923 and 65.22%, respectively.ConclusionThe ADC-based radiomics features may be used to assist clinicians for differential diagnosis of PA and WT from MTs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Wen

Zhang²,

Zhu³

et al. 2022

Front. Oncol.

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“…It provides more objective and quantitative information regarding tumor heterogeneity beyond visual inspection [10]. TA has demonstrated its superiority in differentiating and grading tumors, or predicting treatment response in various diseases [10][11][12]. However, to the best of our knowledge, no study has applied TA of MRI to differentiate sinonasal NHL from SCC until now.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Eur Arch Otorhinolaryngol

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Background: 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: Twenty-one patients with sinonasal SCC and 15 patients with NHL were retrospectively enrolled. TAs were performed on T2-weighted image (T2WI), apparent diffusion coe cient (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 signi cantly 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; Speci city, 0.67; Cut-off 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.

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“…In the different fields of medicine, texture studies integrate the micro-and macrostructures of healthy and unhealthy tissues, where computer systems are used to reduce the human error factor [11]. For instance, variations on image texture have been quantified through regularity measurements, such as in dermatology, to categorize skin lesions [12,13]; magnetic resonance images to recognize and differentiate brain tumors from other tissues, such as gray matter, white matter, and cerebrospinal fluid [14]; identification lesions in parotid salivary glands [15]; and stability in dental implants [16].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Espinosa

Bailón

Laguna

2021

Entropy

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Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

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Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

Cited by 18 publications

References 36 publications

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

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

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

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

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