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A novel prediction model based on quantitative texture analysis of sonographic images for malignant major salivary glandular tumors
Wu-Chia Lo1, Ping-Chia Cheng2, Wan-Lun Hsu3, Po-Wen Cheng4, Li-Jen Liao5
1 Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei; Head and Neck Cancer Surveillance and Research Study Group, Far Eastern Memorial Hospital, New Taipei City; Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan
2 Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei; Head and Neck Cancer Surveillance and Research Study Group, Far Eastern Memorial Hospital, New Taipei City; Department of Communication Engineering, Asia Eastern University of Science and Technology, New Taipei, Taiwan
3 Genomics Research Center, Academia Sinica, Taipei, Taiwan
4 Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
5 Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei; Head and Neck Cancer Surveillance and Research Study Group, Far Eastern Memorial Hospital, New Taipei City; Department of Electrical Engineering, Yuan Ze University, Taoyuan; Biomedical Engineering Office, Far Eastern Memorial Hospital, Taipei, Taiwan
Correspondence Address:
Li-Jen Liao,
Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Road, Banqiao Dist., New Taipei City 220
Taiwan
 Source of Support: None, Conflict of Interest: None
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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.
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