A novel prediction model based on quantitative texture analysis of sonographic images for malignant major salivary glandular tumors

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