Machine Learning for Opportunistic Screening for Osteoporosis from CT Scans of the Wrist and Forearm.

Ronnie Sebro,Cynthia De La Garza-Ramos

doi:10.3390/diagnostics12030691

Abstract

Background: We investigated whether opportunistic screening for osteoporosis can be done from computed tomography (CT) scans of the wrist/forearm using machine learning. Methods: A retrospective study of 196 patients aged 50 years or greater who underwent CT scans of the wrist/forearm and dual-energy X-ray absorptiometry (DEXA) scans within 12 months of each other was performed. Volumetric segmentation of the forearm, carpal, and metacarpal bones was performed to obtain the mean CT attenuation of each bone. The correlations of the CT attenuations of each of the wrist/forearm bones and their correlations to the DEXA measurements were calculated. The study was divided into training/validation (n = 96) and test (n = 100) datasets. The performance of multivariable support vector machines (SVMs) was evaluated in the test dataset and compared to the CT attenuation of the distal third of the radial shaft (radius 33%). Results: There were positive correlations between each of the CT attenuations of the wrist/forearm bones, and with DEXA measurements. A threshold hamate CT attenuation of 170.2 Hounsfield units had a sensitivity of 69.2% and a specificity of 77.1% for identifying patients with osteoporosis. The radial-basis-function (RBF) kernel SVM (AUC = 0.818) was the best for predicting osteoporosis with a higher AUC than other models and better than the radius 33% (AUC = 0.576) (p = 0.020). Conclusions: Opportunistic screening for osteoporosis could be performed using CT scans of the wrist/forearm. Multivariable machine learning techniques, such as SVM with RBF kernels, that use data from multiple bones were more accurate than using the CT attenuation of a single bone.

Highlights

Bone mineral density (BMD) decreases with age, with the decrease being more evident and rapid in post-menopausal females [1–3]
Since we evaluated the predictive properties of several different bones in the wrist/ forearm to predict osteoporosis and osteopenia/osteoporosis, we used several machine learning methods to select the best combination of bones and clinical factors that could be used to categorize a patient as (i) osteoporotic, (ii) osteopenic/osteoporotic using the World Health Organization (WHO) guidelines, (iii) femoral neck BMD T-score ≤ −2.5, and (iv) femoral neck BMD T-score < −1
We found that the strongest correlations between the total hip BMD T-score and computed tomography (CT) attenuation of the wrist/forearm bones were with the CT attenuation of the scaphoid (r = 0.48, p < 0.001), pisiform (r = 0.43, p < 0.001), and first metacarpal (r = 0.40, p < 0.001)

Summary

Introduction

Bone mineral density (BMD) decreases with age, with the decrease being more evident and rapid in post-menopausal females [1–3]. Dual-energy X-ray absorptiometry (DEXA) is the gold standard screening test for the evaluation of BMD [11]. DEXA evaluates BMD in the L1–L4 lumbar spine, total hip, and femoral neck and compares these values to those of normal young adults (20–29 years of age) from the National Health and Nutrition Examination Survey (NHANES) III cohort to create BMD T-scores [12,13]. We investigated whether opportunistic screening for osteoporosis can be done from computed tomography (CT) scans of the wrist/forearm using machine learning. Methods: A retrospective study of 196 patients aged 50 years or greater who underwent CT scans of the wrist/forearm and dual-energy X-ray absorptiometry (DEXA) scans within 12 months of each other was performed. The performance of multivariable support vector machines (SVMs) was evaluated in the test dataset and compared to the CT attenuation of the distal third of the radial shaft (radius 33%). Multivariable machine learning techniques, such as SVM with RBF kernels, that use data from multiple bones were more accurate than using the CT attenuation of a single bone

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Discussion

Conclusion