A machine-learning approach using pubic CT based on radiomics to estimate adult ages

Purpose: Adult skeletal age estimation is an active research field. To evaluate the performance of a pubic CT radiomics-based machine learning model for estimating age, we established a multiple linear regression model based on radiomics and machine learning methods. Methods: A total of 355 subjects were enrolled in this retrospective study from August 2016 to August 2021, and divided into a training cohort (N = 325) and a testing cohort (N = 30). Computerized texture analysis of the semi-automatically segmentation was performed and 107 texture features were extracted from the regions. Then we used univariate linear regression and multivariate stepwise regression to assess correlations of texture pa-rameters with age. The most vital features were used to make the best predictive model. Eventually, the established radiomics model was tested with an additional 30 patients. Results: Clinical characteristics include age, sex, height, weight and BMI were not statistically significant different between training and testing cohort (p = 0.098-0.888). Through a multivariate regression analysis using step-wise regression, six texture parameters were found to have significant correlations with age. The regression formula estimating the age was constructed. Conclusions: The radiomics model using machine learning is considered as a new approach for age estimation from pubic symphysis CT features. Digital osteology is obtained in a non-invasive way so that it can be an ideal collection for anthropological studies.