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.

Automated summary

This study aimed to evaluate the performance of a pubic CT radiomics-based machine learning model in estimating adult skeletal age. By analyzing the correlation between texture features and age, the best predictive model was established and its reliability was validated in additional patients. This radiomics model provides a new approach for non-invasive exploration of digital osteology and age estimation.