DOI QR코드

DOI QR Code

Maturation Disparity between Hand-Wrist Bones in a Chinese Sample of Normal Children: An Analysis Based on Automatic BoneXpert and Manual Greulich and Pyle Atlas Assessment

  • Zhang, Ji (Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine) ;
  • Lin, Fangqin (Department of Radiology, Shanghai Children's Hospital, Shanghai Jiao Tong University) ;
  • Ding, Xiaoyi (Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine)
  • 투고 : 2015.03.24
  • 심사 : 2016.02.03
  • 발행 : 2016.06.01

초록

Objective: To assess the maturation disparity of hand-wrist bones using the BoneXpert system and Greulich and Pyle (GP) atlas in a sample of normal children from China. Materials and Methods: Our study included 229 boys and 168 girls aged 2-14 years. The bones in the hand and wrist were divided into five groups: distal radius and ulna, metacarpals, proximal phalanges, middle phalanges and distal phalanges. Bone age (BA) was assessed separately using the automatic BoneXpert and GP atlas by two raters. Differences in the BA between the most advanced and retarded individual bones and bone groups were analyzed. Results: In 75.8% of children assessed with the BoneXpert and 59.4% of children assessed with the GP atlas, the BA difference between the most advanced and most retarded individual bones exceeded 2.0 years. The BA mean differences between the most advanced and most retarded individual bones were 2.58 and 2.25 years for the BoneXpert and GP atlas methods, respectively. Furthermore, for both methods, the middle phalanges were the most advanced group. The most retarded group was metacarpals for BoneXpert, while metacarpals and the distal radius and ulna were the most retarded groups according to the GP atlas. Overall, the BAs of the proximal and distal phalanges were closer to the chronological ages than those of the other bone groups. Conclusion: Obvious and regular maturation disparities are common in normal children. Overall, the BAs of the proximal and distal phalanges are more useful for BA estimation than those of the other bone groups.

키워드

참고문헌

  1. Greulich WW, Pyle SI. Radiographic Atlas of Skeletal Development of the Hand and Wrist, 2nd ed. Stanford, CA: Stanford University Press, 1959
  2. Kim JR, Lee YS, Yu J. Assessment of bone age in prepubertal healthy Korean children: comparison among the Korean standard bone age chart, Greulich-Pyle method, and Tanner-Whitehouse method. Korean J Radiol 2015;16:201-205 https://doi.org/10.3348/kjr.2015.16.1.201
  3. Ashizawa K, Kumakura C, Zhou X, Jin F, Cao J. RUS skeletal maturity of children in Beijing. Ann Hum Biol 2005;32:316-325 https://doi.org/10.1080/03014460500087725
  4. Zhang SY, Liu LJ, Wu ZL, Liu G, Ma ZG, Shen XZ, et al. Standards of TW3 skeletal maturity for Chinese children. Ann Hum Biol 2008;35:349-354 https://doi.org/10.1080/03014460801953781
  5. Hsieh CW, Liu TC, Wang JK, Jong TL, Tiu CM. Simplified radius, ulna, and short bone-age assessment procedure using grouped-Tanner-Whitehouse method. Pediatr Int 2011;53:567-575 https://doi.org/10.1111/j.1442-200X.2011.03378.x
  6. Thangam P, Mahendiran TV, Thanushkodi K. Skeletal bone age assessment - research directions. J Eng Sci Technol Rev 2012;2:90-96
  7. Thodberg HH. Clinical review: an automated method for determination of bone age. J Clin Endocrinol Metab 2009;94:2239-2244 https://doi.org/10.1210/jc.2008-2474
  8. Martin DD, Sato K, Sato M, Thodberg HH, Tanaka T. Validation of a new method for automated determination of bone age in Japanese children. Horm Res Paediatr 2010;73:398-404 https://doi.org/10.1159/000308174
  9. Kaplowitz P, Srinivasan S, He J, McCarter R, Hayeri MR, Sze R. Comparison of bone age readings by pediatric endocrinologists and pediatric radiologists using two bone age atlases. Pediatr Radiol 2011;41:690-693 https://doi.org/10.1007/s00247-010-1915-0
  10. Zhang SY, Liu G, Ma CG, Han YS, Shen XZ, Xu RL, et al. Automated determination of bone age in a modern Chinese population. ISRN Radiol 2013;2013:874570
  11. Thodberg HH, Savendahl L. Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 2010;17:1425-1432 https://doi.org/10.1016/j.acra.2010.06.007
  12. Vejvoda M, Grant DB. Discordant bone maturation of the hand in children with precocious puberty and congenital adrenal hyperplasia. Acta Paediatr Scand 1981;70:903-905 https://doi.org/10.1111/j.1651-2227.1981.tb06248.x
  13. Jimenez-Castellanos J, Carmona A, Catalina-Herrera CJ, Vinuales M. Skeletal maturation of wrist and hand ossification centers in normal Spanish boys and girls: a study using the Greulich-Pyle method. Acta Anat (Basel) 1996;155:206-211 https://doi.org/10.1159/000147806
  14. Lee MM. Maturation disparity between hand-wrist bones in Hong Kong Chinese children. Am J Phys Anthropol 1971;34:385-395 https://doi.org/10.1002/ajpa.1330340308
  15. Carpenter CT, Lester EL. Skeletal age determination in young children: analysis of three regions of the hand/wrist film. J Pediatr Orthop 1993;13:76-79 https://doi.org/10.1097/01241398-199301000-00015
  16. Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 2009;28:52-66 https://doi.org/10.1109/TMI.2008.926067
  17. van Rijn RR, Thodberg HH. Bone age assessment: automated techniques coming of age? Acta Radiol 2013;54:1024-1029 https://doi.org/10.1258/ar.2012.120443
  18. Erhart S, Lutz M, Arora R, Schmoelz W. Measurement of intraarticular wrist joint biomechanics with a force controlled system. Med Eng Phys 2012;34:900-905 https://doi.org/10.1016/j.medengphy.2011.10.003
  19. de Bruin M, van de Giessen M, Vroemen JC, Veeger HE, Maas M, Strackee SD, et al. Geometrical adaptation in ulna and radius of cerebral palsy patients: measures and consequences. Clin Biomech (Bristol, Avon) 2014;29:451-457 https://doi.org/10.1016/j.clinbiomech.2014.01.003
  20. Daly RM, Saxon L, Turner CH, Robling AG, Bass SL. The relationship between muscle size and bone geometry during growth and in response to exercise. Bone 2004;34:281-287 https://doi.org/10.1016/j.bone.2003.11.009

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