Fa yi xue za zhi · 2024
MRI Application in Quantification of Epiphyseal Development in the Wrist and Bone Age Estimation of Han Male Adolescents in East China.
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Zhou Zhi-Lu, Zhang Dong-Fei, Chen Jie-Min, Wang Ya-Hui, Hao Hong-Xia, Liu Tai-Ang, He Yu-Heng, Long Ding-Nian, Liu Rui-Jue, Wan Lei
Аннотация
OBJECTIVES: To investigate the value of wrist MRI in bone age estimation for male adolescents in Shanghai, Zhejiang and Jiangsu.
METHODS: A total of 124 Han male adolescents aged 6.0 to 18.0 years from Shanghai, Zhejiang and Jiangsu were selected as subjects. Their weight and height were measured, and T1WI and T2WI sequences of the wrist were scanned. The distal ends of the radius and ulna, and the first to five metacarpal epiphyses and corresponding metaphyses were selected as observational indexes after MRI images of the wrist were obtained. The development of each index was classified (0-2 grades) by a deputy senior imaging expert, then the maximum width of each index was measured by another deputy senior expert. Height, weight, classification and maximum width of indexes were used as input variables, and age was used as the target variable. Support vector machine, random forest, current reality tree, and linear regression models were established to estimate the bone age, and the model with the highest accuracy was selected.
RESULTS: The height, weight, classification of wrist bone epiphysis development, maximum width of each bone metaphysis and epiphysis were all correlated with age (P<0.05). The accuracies of the support vector machine were the highest when the differences between bone age and actual chronological age were within 1.0 and 1.5 years (88.7% and 96.0%, respectively).
CONCLUSIONS: It is feasible to estimate bone age by using MRI images. Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.
METHODS: A total of 124 Han male adolescents aged 6.0 to 18.0 years from Shanghai, Zhejiang and Jiangsu were selected as subjects. Their weight and height were measured, and T1WI and T2WI sequences of the wrist were scanned. The distal ends of the radius and ulna, and the first to five metacarpal epiphyses and corresponding metaphyses were selected as observational indexes after MRI images of the wrist were obtained. The development of each index was classified (0-2 grades) by a deputy senior imaging expert, then the maximum width of each index was measured by another deputy senior expert. Height, weight, classification and maximum width of indexes were used as input variables, and age was used as the target variable. Support vector machine, random forest, current reality tree, and linear regression models were established to estimate the bone age, and the model with the highest accuracy was selected.
RESULTS: The height, weight, classification of wrist bone epiphysis development, maximum width of each bone metaphysis and epiphysis were all correlated with age (P<0.05). The accuracies of the support vector machine were the highest when the differences between bone age and actual chronological age were within 1.0 and 1.5 years (88.7% and 96.0%, respectively).
CONCLUSIONS: It is feasible to estimate bone age by using MRI images. Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.