Prediction of osteoporosis from proximal femoral cortical bone thickness and Hounsfield unit value with clinical significance

Xu, Gaoxiang; Wang, Daofeng; Zhang, Hao; Xu, Cheng; Li, Hua; Zhang, Wupeng; Li, Jiantao; Zhang, Licheng; Tang, Peifu

doi:10.3389/fsurg.2022.1047603

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Multiple studies have used the Hu value to determine osteoporosis demonstrated the prediction value of CT in osteoporosis. 29 , 30 , 31 Because CT has higher imaging resolution in identifying bone trabeculae, bone density, and bone cortex, our model is more accurate in predicting the probability of re‐fracture compared to previous studies using X‐rays.…”

Section: Discussionmentioning

confidence: 98%

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Zhu,

Liu,

Liu

et al. 2024

Orthopaedic Surgery

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BackgroundThe reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X‐ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT).ObjectiveTo construct a novel predicting model based on bone turn‐over markers and CT to identify patients who were more inclined to suffer spine refracture.MethodsCT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U‐net model was adopted to automatically segment ROI. Three‐dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121‐3D model to model the cropped region and simultaneously build a T‐NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models.ResultsThe performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer–Lemeshow goodness‐of‐fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p‐value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly.ConclusionT_P1NT was an independent risk factor of refracture. Our 3D‐FULL model showed better performance in predicting high‐risk population of spine refracture than other models and junior doctors do. This model can be applicable to real‐world translation due to its automatic segmentation and detection.

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Section: Discussionmentioning

confidence: 98%

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Zhu,

Liu,

Liu

et al. 2024

Orthopaedic Surgery

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“…Additionally, in-scan calibration prevents a retrospective application. In response to this limitation, alternative indicators for bone density, including HU-based measurements and cortical thickness ratio, have been suggested [8][9][10][11]. However, these methods are subject to various limitations.…”

Section: Introductionmentioning

confidence: 99%

Predictive Value of Dual-Energy CT-Derived Metrics for the Use of Bone Substitutes in Distal Radius Fracture Surgery

Reschke,

Koch,

Mahmoudi

et al. 2024

Diagnostics

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(1) Background: Low bone mineral density (BMD) is a significant risk factor for complicated surgery and leads to the increased use of bone substitutes in patients with distal radius fractures (DRFs). No accepted model has yet been established to predict the use of bone substitutes to facilitate preoperative planning. (2) Methods: Unenhanced dual-energy CT (DECT) images of DRFs were retrospectively acquired between March 2016 and September 2020 using the internal PACS system. Available follow-up imaging and medical health records were reviewed to determine the use of bone substitutes. DECT-based BMD, trabecular Hounsfield units (HU), cortical HU, and cortical thickness ratio were measured in non-fractured segments of the distal radius. Diagnostic accuracy parameters were calculated for all metrics using receiver-operating characteristic (ROC) curves and associations of all metrics with the use of bone substitutes were evaluated using logistic regression models. (3) The final study population comprised 262 patients (median age 55 years [IQR 43–67 years]; 159 females, 103 males). According to logistic regression analysis, DECT-based BMD was the only metric significantly associated with the use of bone substitutes (odds ratio 0.96, p = 0.003). However, no significant associations were found for cortical HU (p = 0.06), trabecular HU (p = 0.33), or cortical thickness ratio (p = 0.21). ROC-curve analysis revealed that a combined model of all four metrics had the highest diagnostic accuracy with an area under the curve (AUC) of 0.76. (4) Conclusions: DECT-based BMD measurements performed better than HU-based measurements and cortical thickness ratio. The diagnostic performance of all four metrics combined was superior to that of the individual parameters.

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Prediction of osteoporosis from proximal femoral cortical bone thickness and Hounsfield unit value with clinical significance

Cited by 2 publications

References 33 publications

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Predictive Value of Dual-Energy CT-Derived Metrics for the Use of Bone Substitutes in Distal Radius Fracture Surgery

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