Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Yinxia Zhao; Tianyun Zhao; Shenglan Chen; Xintao Zhang; Mario Serrano Sosa; Jin Liu; Xianfu Mo; Xiaojun Chen; Mingqian Huang; Shaolin Li; Xiaodong Zhang; Chuan Huang

doi:10.21037/qims-21-587

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Quant Imaging Med Surg. 2022 Feb;12(2):1198-1213. doi: 10.21037/qims-21-587.

Authors

Yinxia Zhao^#¹, Tianyun Zhao^#², Shenglan Chen³, Xintao Zhang¹, Mario Serrano Sosa², Jin Liu⁴, Xianfu Mo¹, Xiaojun Chen⁴, Mingqian Huang⁵, Shaolin Li⁴, Xiaodong Zhang¹, Chuan Huang^{2

6}

Affiliations

¹ Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China.
² Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
³ Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
⁴ Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
⁵ Department of Radiology, The Mount Sinai Hospital, New York, NY, USA.
⁶ Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA.

^# Contributed equally.

Abstract

Background: Although lumbar bone marrow fat fraction (BMFF) has been demonstrated to be predictive of osteoporosis, its utility is limited by the requirement of manual segmentation. Additionally, quantitative features beyond simple BMFF average remain to be explored. In this study, we developed a fully automated radiomic pipeline using deep learning-based segmentation to detect osteoporosis and abnormal bone density (ABD) using a <20 s modified Dixon (mDixon) sequence.

Methods: In total, 222 subjects underwent quantitative computed tomography (QCT) and lower back magnetic resonance imaging (MRI). Bone mineral density (BMD) were extracted from L1-L3 using QCT as the reference standard; 206 subjects (48.8±14.9 years old, 140 females) were included in the final analysis, and were divided temporally into the training/validation set (142/64 subjects). A deep-learning network was developed to perform automated segmentation. Radiomic models were built using the same training set to predict ABD and osteoporosis using the mDixon maps. The performance was evaluated using the temporal validation set comprised of 64 subjects, along with the automated segmentation. Additional 25 subjects (56.1±8.8 years, 14 females) from another site and a different scanner vendor was included as independent validation to evaluate the performance of the pipeline.

Results: The automated segmentation achieved an outstanding mean dice coefficient of 0.912±0.062 compared to manual in the temporal validation. Task-based evaluation was performed in the temporal validation set, for predicting ABD and osteoporosis, the area under the curve, sensitivity, specificity, and accuracy were 0.925/0.899, 0.923/0.667, 0.789/0.873, 0.844/0.844, respectively. These values were comparable to that of manual segmentation. External validation (cross-vendor) was also performed; the area under the curve, sensitivity, specificity, and accuracy were 0.688/0.913, 0.786/0.857, 0.545/0.944, 0.680/0.920 for ABD and osteoporosis prediction, respectively.

Conclusions: Our work is the first attempt using radiomics to predict osteoporosis with BMFF map, and the deep-learning based segmentation will further facilitate the clinical utility of the pipeline as a screening tool for early detection of ABD.

Keywords: Osteoporosis; chemical shift imaging; deep learning.