In Dento maxillo facial radiology
OBJECTIVE : This study aimed to identify robust radiomic features in multi ultrasonography of the submandibular gland and normalize the inter device discrepancies by applying a machine-learning based harmonization method.
METHODS : Ultrasonographic images of normal submandibular gland of young healthy adults, aged between 20 and 40 years, were selected from two different devices. In a total of 30 images, the region of interest was determined along the border of gland parenchyma, and 103 radiomic features were extracted using A-VIEW. The coefficient of variation (CV) was obtained for individual features, and the features showing CV less than 10% were selected. For the selected features, the inter device discrepancy was normalized using machine-learning method, called the ComBat harmonization. Median differences of the features between the two scanners, before and after harmonization, were compared using Mann-Whitney U test; confidence interval of 95%.
RESULTS : Among total 103 radiomic features, 17 features were selected as robust, showing CV less than 10% in both scanners. All values of selected features, except two, showed a statistical difference between the two devices. After applying the ComBat harmonization method, the median and distribution of the 16 features were harmonized to show no significant difference between the two scanners (p > 0.05). One feature remained different (p < 0.05).
CONCLUSION : On ultrasonographic examination, robust radiomic features for normal submandibular gland were obtained and inter device normalization was efficiently conducted using ComBat harmonization. Our findings would be useful for multi devices or multicenter studies based on clinical ultrasonographic imaging data to improve the accuracy of the overall diagnostic model.
Choi Yoon Joo, Jeon Kug Jin, Lee Ari, Han Sang-Sun, Lee Chena
2022-Nov-07
Feature selection, Machine-learning, Radiomics, Salivary gland, ultrasonography
