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Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation

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A Correction to this article was published on 01 April 2024

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Abstract

Purpose

This study aimed to apply a newly developed semi-automatic phantom-less QCT (PL-QCT) to measure proximal humerus trabecular bone density based on chest CT and verify its accuracy and precision.

Methods

Subcutaneous fat of the shoulder joint and trapezius muscle were used as calibration references for PL-QCT BMD measurement. A self-developed algorithm based on a convolution map was utilized in PL-QCT for semi-automatic BMD measurements. CT values of ROIs used in PL-QCT measurements were directly used for phantom-based quantitative computed tomography (PB-QCT) BMD assessment. The study included 376 proximal humerus for comparison between PB-QCT and PL-QCT. Two sports medicine doctors measured the proximal humerus with PB-QCT and PL-QCT without knowing each other’s results. Among them, 100 proximal humerus were included in the inter-operative and intra-operative BMD measurements for evaluating the repeatability and reproducibility of PL-QCT and PB-QCT.

Results

A total of 188 patients with 376 shoulders were involved in this study. The consistency analysis indicated that the average bias between proximal humerus BMDs measured by PB-QCT and PL-QCT was 1.0 mg/cc (agreement range – 9.4 to 11.4; P > 0.05, no significant difference). Regression analysis between PB-QCT and PL-QCT indicated a good correlation (R-square is 0.9723). Short-term repeatability and reproducibility of proximal humerus BMDs measured by PB-QCT (CV: 5.10% and 3.41%) were slightly better than those of PL-QCT (CV: 6.17% and 5.64%).

Conclusions

We evaluated the bone quality of the proximal humeral using chest CT through the semi-automatic PL-QCT system for the first time. Comparison between it and PB-QCT indicated that it could be a reliable shoulder BMD assessment tool with acceptable accuracy and precision.

Summary

This study developed and verify a semi-automatic PL-QCT for assessment of proximal humeral bone density based on CT to assist in the assessment of proximal humeral osteoporosis and development of individualized treatment plans for shoulders.

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Data availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the restriction of IRB.

Change history

Abbreviations

BMD:

Bone mineral density

vBMD:

Volumetric bone mineral density

DXA:

Dual-energy X-ray absorptiometry

QCT:

Quantitative computed tomography

PB-QCT:

Phantom-based quantitative computed tomography

PL-QCT:

Phantom-less quantitative computed tomography

LDCT:

Low-dose chest computed tomography

ROI:

Region of interest

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Funding

This research was funded by The National Natural Science Foundation of China (U21A20390), the Special Foundation for Science and Technology Innovation of Jilin (20200201566JC, 20230204075YY), the Health Service Capacity Building Projects of Jilin Province (05KA001026009002), Graduate Innovation Program of Jilin University (No. 2023CX122), the 2023 Science and Technology Project of Jilin Provincial Department of Education (JJKH20231226KJ), and Jilin Province Natural Science Foundation (20190201218JC).

Author information

Author notes

  1. De-Ming Guo and Yuan-Zhi Weng contribute equally to this work.

Authors and Affiliations

  1. Orthopaedic Medical Center, The Second Norman Bethune Hospital of Jilin University, Changchun, People’s Republic of China

    De-Ming Guo, Ze-Hao Yu, Shi-Huai Li, Xiao-Ning Liu, Xiong-Feng Tang, Rui-Yan Li & Yan-Guo Qin

  2. Jilin Provincial Key Laboratory of Orthopaedics, Changchun, People’s Republic of China

    De-Ming Guo, Ze-Hao Yu, Shi-Huai Li, Wen-Rui Qu, Xiao-Ning Liu, Xiong-Feng Tang, Rui-Yan Li & Yan-Guo Qin

  3. Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of Ministry of Education, Jilin University, Changchun, 130041, Jilin Province, China

    De-Ming Guo, Ze-Hao Yu, Shi-Huai Li, Wen-Rui Qu, Xiong-Feng Tang, Rui-Yan Li, Hao Xu & Yan-Guo Qin

  4. Orthopaedic and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China

    Yuan-Zhi Weng, Chi Ma & Weijia William Lu

  5. Department of Hand Surgery, The Second Norman Bethune Hospital of Jilin University, Changchun, People’s Republic of China

    Wen-Rui Qu

  6. Bone’s Technology Limited, Shenzhen, Hong Kong, People’s Republic of China

    Huan Qi

  7. Radiology Department, The Second Norman Bethune Hospital of Jilin University, Changchun, People’s Republic of China

    Qinghe Han

  8. College of Computer Science and Technology, Jilin University, Changchun, People’s Republic of China

    Hao Xu

Authors
  1. De-Ming Guo
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  8. Chi Ma
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  11. Qinghe Han
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  12. Hao Xu
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  13. Weijia William Lu
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  14. Yan-Guo Qin
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Corresponding authors

Correspondence to Weijia William Lu or Yan-Guo Qin.

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The original online version of this article was revised: In this article the affiliation details for Yuan-Zhi Weng and Chi Ma were incorrectly given as 'Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of Ministry of Education, Jilin University, Changchun 130041, Jilin Province, China' but should have been 'Orthopaedic and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China'.

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Guo, DM., Weng, YZ., Yu, ZH. et al. Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation. Osteoporos Int 35, 1049–1059 (2024). https://doi.org/10.1007/s00198-024-07047-y

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