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. 2023 Jan 24;31(3):218-222.
doi: 10.4103/jmu.jmu_65_22. eCollection 2023 Jul-Sep.

A Novel Prediction Model Based on Quantitative Texture Analysis of Sonographic Images for Malignant Major Salivary Glandular Tumors

Wu-Chia Lo  1   2   3 , Ping-Chia Cheng  1   2   4 , Wan-Lun Hsu  5 , Po-Wen Cheng  1 , Li-Jen Liao  1   2   6   7
Affiliations

A Novel Prediction Model Based on Quantitative Texture Analysis of Sonographic Images for Malignant Major Salivary Glandular Tumors

Wu-Chia Lo et al. J Med Ultrasound. .

Abstract

Background: The aim of this study was to compare multiple objective ultrasound (US) texture features and develop an objective predictive model for predicting malignant major salivary glandular tumors.

Methods: From August 2007 to May 2018, 144 adult patients who had major salivary gland tumors and subsequently underwent surgery were recruited for this study. Representative brightness mode US pictures were selected for texture analysis and used to develop a prediction model.

Results: We found that the grayscale intensity and standard deviation of the intensity were significantly different between malignant and pleomorphic adenomas. The contrast, inverse difference (INV) movement, entropy, dissimilarity, and INV also differed significantly between benign and malignant tumors. We used stepwise selection of predictors to develop an objective predictive model, as follows: Score = 1.138 × Age - 1.814 × Intensity + 1.416 × Entropy + 1.714 × Contrast. With an optimal cutoff of 0.58, the diagnostic performance of this model had a sensitivity, specificity, overall accuracy, and area under the curve of 83% (95% confidence interval [CI]: 74%-92%), 74% (65%-84%), 78% (72%-85%), and 0.86 (0.80-0.92), respectively.

Conclusion: We have developed a novel computerized diagnostic model based on objective US features to predict malignant major salivary gland tumor. Further improving the computer-aided diagnosis model might change the US examination for major salivary gland tumors in the future.

Keywords: Prediction model; salivary gland tumors; texture; ultrasound.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1
Figure 1
A square block was sampled from a patient with a right parotid tumor (a). A representative US picture was selected for further texture analysis (b). The pathologic report revealed pleomorphic adenoma. Another square block was sampled from a patient with a right parotid tumor with a diagnosis of mucoepidermoid carcinoma (c). Another representative US picture was selected for further texture analysis (d). US: Ultrasound
Figure 2
Figure 2
Comparisons of intensity (a) and SD of intensity (b) between malignant and benign tumors. SD: Standard deviation, Mali.: Malignant, Pleo.: Pleomorphic adenoma
Figure 3
Figure 3
ROC analysis of the objective prediction model (AUC = 0.86). ROC: Receiver operating curve, AUC: Area under the curve
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References

    1. Cheng PC, Chang CM, Huang CC, Lo WC, Huang TW, Cheng PW, et al. The diagnostic performance of ultrasonography and computerized tomography in differentiating superficial from deep lobe parotid tumours. Clin Otolaryngol. 2019;44:286–92. - PubMed
    1. Katz P, Hartl DM, Guerre A. Clinical ultrasound of the salivary glands. Otolaryngol Clin North Am. 2009;42:973–1000. - PubMed
    1. Lo WC, Chang CM, Wang CT, Cheng PW, Liao LJ. A novel sonographic scoring model in the prediction of major salivary gland tumors. Laryngoscope. 2021;131:E157–62. - PubMed
    1. Martins de Almeida M. Use of statistical techniques to analyze textures in medical images for tumor detection and evaluation. Adv Mol Imaging Interv Radiol. 2018;1:01–6.
    1. Yonetsu K, Ohki M, Kumazawa S, Eida S, Sumi M, Nakamura T. Parotid tumors:Differentiation of benign and malignant tumors with quantitative sonographic analyses. Ultrasound Med Biol. 2004;30:567–74. - PubMed