Predicting Bone Marrow Edema and Fracture Age in Vertebral Fragility Fractures Using MDCT

Feasibility study in quantitative identification of fresh and old osteoporotic vertebral compression fracture by vertebral CT value and CT value difference

BACKGROUND

This study aims to assess the diagnostic utility of vertebral CT value and CT value difference in distinguishing between fresh and old osteoporotic vertebral compression fractures (OVCF).

METHODS

A retrospective analysis was conducted on 118 patients with OVCF who underwent both MRI and CT examinations at our hospital. The nature of the fractured vertebra was determined according to MRI. The CT value of the fractured vertebrae and the mean CT value of the adjacent normal vertebrae were measured separately, and the differences between these values were calculated. Independent samples t-tests were used to compare CT value and CT value difference among all groups. The receiver operating characteristic (ROC) curve was employed to determine the optimal cut-off value for both CT value and CT value difference in differentiating fresh and old fractures.

RESULTS

The study included a total of 163 fractured vertebrae from 118 patients. The CT value of fresh fractured vertebrae was significantly higher than those of adjacent normal vertebrae, which was statistically different (P < 0.001). In contrast, the difference between CT value of old fractured vertebrae and those of adjacent normal vertebrae was not statistically significant (P > 0.05). There were significant differences in CT value and CT value difference between fresh fractured vertebrae and old fractured vertebrae (P < 0.001). The ROC curve analysis showed that the optimal cut-off value of CT value for fresh fractures and old fractures was 103.40 HU. The optimal cut-off value of CT value difference was 39.81 HU.

CONCLUSION

Vertebral CT value and CT value difference offer a certain reference value for distinguishing between fresh and old OVCF. These parameters can serve as a rapid diagnostic tool when MRI is unavailable or impractical, aiding in the timely assessment of OVCF.

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study

RATIONALE AND OBJECTIVES

To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs).

METHODS

A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA).

RESULTS

Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models.

CONCLUSION

Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.

Value of Edema-like Marrow Signal Intensity in Diagnosis of Joint Pain: Radiologists' Perspective

Musculoskeletal pain is a significant contributor to disability. The mechanism and target of the treatment should be optimized by imaging, but currently no accepted gold standard exists to image pain. In addition to end-organ pathology, other mediators also contribute to nociception, such as angiogenesis, axonal extension, immunologic modulation, and central sensitization. Recent research indicates that local inflammation is a significant contributor to pain in the extremities; therefore, we focus here on edema-like marrow signal intensity (ELMSI). We examine both the relevance of ELMSI for pain and novel imaging techniques.

Preliminary study for prediction of benign vertebral compression fracture age by quantitative water fraction using modified Dixon sequences: an imaging biomarker of fracture age

PURPOSE

This study aimed to evaluate whether quantitative water fraction parameters could predict fracture age in patients with benign vertebral compression fractures (VCFs).

METHODS

A total of 38 thoracolumbar VCFs in 27 patients imaged using modified Dixon sequences for water fraction quantification on 3-T MRI were retrospectively reviewed. To calculate quantitative parameters, a radiologist independently measured the regions of interest in the bone marrow edema (BME) of the fractures. Furthermore, five features (BME, trabecular fracture line, condensation band, cortical or end plate fracture line, and paravertebral soft-tissue change) were analyzed. The fracture age was evaluated based on clear-onset symptoms and previously available images. A correlation analysis between the fracture age and water fraction was evaluated using a linear regression model, and a multivariable analysis of the dichotomized fracture age model was performed.

RESULTS

The water fraction ratio was the only significant factor and was negatively correlated with the fracture age of VCFs in multiple linear regression (p = 0.047), whereas the water fraction was not significantly correlated (p = 0.052). Water fraction and water fraction ratio were significant factors in differentiating the fracture age of 1 year in multiple logistic regression (odds ratio 0.894, p = 0.003 and odds ratio 0.986, p = 0.019, respectively). Using a cutoff of 0.524 for the water fraction, the area under the curve, sensitivity, and specificity were 0.857, 85.7%, and 87.1%, respectively.

CONCLUSIONS

Water fraction is a good imaging biomarker for the fracture healing process. The water fraction ratio of the compression fractures can be used to predict the fracture age of benign VCFs.

Efficacy and confounding factors of CT attenuation value differences in distinguishing acute and old vertebral compression fractures: a retrospective study

PURPOSE

To evaluate the influence of various factors on CT attenuation values (HUs) of acute and old fracture vertebra, and to determine the efficacy of HU differences (△HUs) in the differentiation of the two type of fractures.

MATERIALS AND METHODS

A total of 113 acute and 71 old fracture vertebrae confirmed by MRI were included. Four HUs measured at the mid-sagittal, upper 1/3 axial, mid-axial, and lower 1/3 axial planes of each vertebra were obtained. The △HUs between fracture vertebra and its control counterpart was calculated. Receiver operating characteristic (ROC) curve analysis was used and the areas under the ROC curve (AUC) were calculated to evaluate the efficacy of HUs and △HUs. To evaluate the effect of height reduction, region, age and gender on HUs and △HUs, one-way analysis of variance, Pearson correlation analysis and t-test were used.

RESULTS

The HUs and △HUs at the upper 1/3 axial plane achieved the highest AUCs of 0.801 and 0.839, respectively. The HUs decreased gradually from Thoracic to Lumbar in control group of acute fracture. While no significant differences were found in the HUs among the 3 localizations in both fracture groups (all P > 0.05). The HUs were negatively correlated with age in all groups. The HUs of male were significantly higher than female patients in all groups (all P < 0.05). While △HU was not significantly different between males and females (all P > 0.05).

CONCLUSION

The vertebral HUs at the upper 1/3 axial plane are more likely to identify acute fractures. △HUs were beneficial in eliminating interfering factors.

Differences in time-to-fusion based on "absence of peri-graft radiolucency" and "trabecular bone bridging" criteria after transforaminal lumbar interbody fusion in patients with low and normal bone density

OBJECTIVE

To investigate the difference in time-to-fusion between two sets of interbody fusion criteria (absence of peri-graft radiolucency vs. trabecular bone bridging), and to determine the effect of osteoporosis on time-to-fusion.

MATERIALS AND METHODS

This retrospective study enrolled 79 patients treated for degenerative disease with one-level transforaminal lumbar interbody fusion from February 2012 to December 2018, and who had both pre- and post-operative CTs. Patients were divided into osteoporosis, osteopenia, and normal groups based on L1 vertebral body attenuation values in pre-operative CT with cutoff of 90 Hounsfield units (HU) and 120 HU. The osteoporosis, osteopenia, and normal groups included 36 patients (mean age: 69.9 years; 8 men and 28 women), 18 patients (mean age: 62.6 years; 7 men and 11 women), and 25 patients (mean age: 56.6 years; 15 men and 10 women), respectively. Fusion was assessed annually on post-operative CT images using absence of peri-graft radiolucency and trabecular bone bridging criteria. Time-to-fusion was estimated using the Kaplan-Meier method, and differences between the groups were examined using the log-rank test. Cox proportional hazards regression was performed.

RESULTS

Time-to-fusion took significantly longer in the osteoporosis group in both fusion criteria (0.5 years in normal vs. 2 years in osteopenia vs. 3 years in osteoporosis for absence of peri-graft radiolucency; p = 0.003, and 3 years vs. 4 years vs. 5 years for trabecular bone bridging; p = 0.001). Only osteoporosis grouping was independent risk factor for slow trabecular bone fusion (hazard ratio:0.339; p = 0.003).

CONCLUSION

The median time to fusion was significantly longer when using trabecular bone bridging criteria than absence of peri-graft radiolucency criteria.

Collapse-related bone changes at multidetector CT in ARCO 1-2 osteonecrotic femoral heads: correlation with clinical and MRI data

OBJECTIVE

To assess the frequency of collapse-related bone changes at multi-detector CT (MDCT) in osteonecrotic femoral heads (ONFH) and to compare clinical parameters and MRI findings in Association Research Circulation Osseous (ARCO) 1-2 ONFH with or without collapse-related bone changes (CRBC) at MDCT.

MATERIALS AND METHODS

This is a secondary analysis of radiographic, MRI, and MDCT examinations of ONFH of patients eligible for a prospective clinical trial. Radiographs and MRI were analyzed to perform ARCO staging. Frequency of CRBC at MDCT including cortical interruption, trabecular interruption, impaction, and resorption was determined by two readers (R1, R2) blinded to radiographic, MRI, and clinical data. Baseline clinical and imaging data of ARCO 1-2 ONFH were compared between hips with or without CRBC at MDCT.

RESULTS

One hundred thirty-two hips of 77 participants were analyzed. There were 78 non-collapsed and 54 collapsed ONFH. For R1 and R2, 31/78 (40%) and 20/78 (26%) ARCO 1-2 ONFH and 54/54 (100%) and 53/54 (98%) ARCO 3-4 ONFH showed at least one CRBC at MDCT. For both readers, there was no significant difference in pain, functional impairment, size of lesion, and the presence of BME on MRI between ARCO 1-2 hips with or without CRBC at MDCT.

CONCLUSION

Twenty-six to forty percent of ARCO 1-2 ONFH demonstrate at least one collapse-related bone change at CT. Their clinical and MRI findings do not differ from those without collapse-related bone changes.

KEY POINTS

• Ninety-eight to one hundred percent of collapsed and 26-40% of non-collapsed osteonecrotic femoral heads presented at least one collapse-related bone change at CT (cortical or trabecular bone interruption, trabecular bone impaction, or resorption). • There was no significant difference in age, sex, pain, functional impairment, size of lesion, or frequency of marrow edema on MRI between non-collapsed hips with or without collapse-related bone changes at CT. • The significance of collapse-related bone changes at CT should be further assessed.

Radiographic assessment of acute vs chronic vertebral compression fractures

PURPOSE

Distinguishing between acute and chronic vertebral compression fractures typically requires advanced imaging techniques such as magnetic resonance imaging (MRI). Recognizing specific radiographic findings associated with fracture acuity may improve the accuracy of radiographic assessment.

METHODS

Patients with compression fractures that had both radiographic and MRI studies of the lumbar spine within a 30-day time frame were retrospectively reviewed. MRI studies were used to determine compression fracture acuity. Radiographs were interpreted by a separate group of radiologists blinded to the MRI results. Radiographic findings of endplate osteophyte, subendplate density, subendplate cleft, and subendplate cyst were recorded as was the overall impression of fracture acuity.

RESULTS

Sensitivity and specificity for radiographic reporting of acute fracture were 0.52 (95% CI: 0.42, 0.61) and 0.95 (95% CI: 0.93, 0.97) respectively. For chronic fractures, the sensitivity and specificity were 0.52 (95% CI: 0.41, 0.63) and 0.94 (95% CI: 0.92, 0.96). The radiographic presence of a subendplate cleft increased the odds of a fracture being acute by a factor of 1.75 (95% CI: 1.09, 2.81; P = 0.0202). The radiographic presence of subendplate density increased the odds of a fracture being acute by a factor of 1.78 (95% CI: 1.21, 2.63; P = 0.0037). The presence of an endplate osteophyte or subendplate cyst was not significantly associated with fracture acuity.

CONCLUSION

Radiographs are relatively insensitive in distinguishing between acute and chronic lumbar compression fractures but the presence of a subendplate cleft or subendplate density increases the likelihood that a given fracture is acute.

Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features

RATIONALE AND OBJECTIVES

To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.

MATERIALS AND METHODS

This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort.

RESULTS

A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts.

CONCLUSION

Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

Differentiation Between Osteoporotic And Neoplastic Vertebral Fractures: State Of The Art And Future Perspectives

Vertebral fractures are a common condition, occurring in the context of osteoporosis and malignancy. These entities affect a group of patients in the same age range; clinical features may be indistinct and symptoms non-existing, and thus present challenges to diagnosis. In this article, we review the use and accuracy of different imaging modalities available to characterize vertebral fracture etiology, from well-established classical techniques, to the role of new and advanced imaging techniques, and the prospective use of artificial intelligence. We also address the role of imaging on treatment. In the context of osteoporosis, the importance of opportunistic diagnosis is highlighted. In the near future, the use of automated computer-aided diagnostic algorithms applied to different imaging techniques may be really useful to aid on diagnosis.