Assessment of Flatfoot Deformity Using Digitally Reconstructed Radiographs: Reliability and Comparison to Conventional Radiographs

Fuller, Robert; Kim, Jaeyoung; An, Tonya; Rajan, Lavan; Cororaton, Agnes D.; Kumar, Prashanth; Deland, Jonathan T.; Ellis, Scott J.

doi:10.1177/10711007221089260

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Lastly, in lieu of using specialized software to compute the 3D rotation of the talus, we decided to measure these axes in the images obtained in a manner similar to how digitally reconstructed radiographs are created from WBCT in order to make our work more straightforward and accessible to those with limited access to WBCT or advanced engineering techniques. 14 Although a future study describing the complete 3D motion of the talus could be useful, the purpose of this study was to investigate the axial plane rotation of the talus that exists in abduction deformity and to argue that this talar rotation should be concurrently corrected to overcome the current limitation of abduction deformity corrective means in PCFD, not to demonstrate the motion of the talus in PCFD in 3 dimensions.…”

Section: Discussionmentioning

confidence: 99%

Axial Plane Rotation of the Talus in Progressive Collapsing Foot Deformity: A Weightbearing Computed Tomography Analysis

et al. 2023

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Background: Progressive collapsing foot deformity (PCFD) is recognized as a 3-dimensional deformity centered around the talus. Previous studies have described some features of talar motion in the ankle mortise in PCFD, such as sagging in the sagittal plane or valgus tilt in the coronal plane. However, axial plane alignment of the talus in the ankle mortise in PCFD has not been investigated extensively. The purpose of this study was to examine this axial plane alignment of PCFD vs controls using weightbearing computed tomography (WBCT) images and to determine if talar rotation in the axial plane is associated with increased abduction deformity, as well as to assess the medial ankle joint space narrowing in PCFD that may be associated with axial plane talar rotation. Methods: Multiplanar reconstructed WBCT images of 79 patients with PCFD and 35 control patients (39 scans) were retrospectively analyzed. The PCFD group was divided into 2 subgroups depending on preoperative talonavicular coverage angle (TNC): moderate abduction (TNC 20-40 degrees, n=57) and severe abduction (TNC >40 degrees, n=22). Using the transmalleolar (TM) axis as a reference, the axial alignment of the talus (TM-Tal), calcaneus (TM-Calc), and second metatarsal (TM-2MT) were calculated. Difference between TM-Tal and TM-Calc was calculated to examine talocalcaneal subluxation. A second method to assess talar rotation within the mortise utilized an angle between the lateral malleolus and the talus (LM-Tal) in the axial slices of WBCT. In addition, the prevalence of medial tibiotalar joint space narrowing was assessed. These parameters were compared between the control and PCFD groups, and between moderate and severe abduction groups. Results: The talus was significantly more internally rotated with respect to the ankle TM axis and the lateral malleolus in PCFD patients compared to controls, and in the severe abduction group compared with the moderate abduction group, using both measurement methods. Axial calcaneal orientation did not differ between groups. There was significantly greater axial talocalcaneal subluxation in the PCFD group, and this was also greater in the severe abduction group. The prevalence of medial joint space narrowing was higher in PCFD patients. Conclusion: Our findings suggest that talar malrotation in the axial plane should be considered an underlying feature of abduction deformity in PCFD. The malrotation occurs in both the talonavicular and ankle joints. This rotational deformity should be corrected at the time of reconstructive surgery, especially in cases of severe abduction deformity. In addition, medial ankle joint narrowing was observed in PCFD patients, with a higher prevalence of medial ankle joint narrowing in those with severe abduction. Level of Evidence: Level III, case-control study.

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Section: Discussionmentioning

confidence: 99%

Axial Plane Rotation of the Talus in Progressive Collapsing Foot Deformity: A Weightbearing Computed Tomography Analysis

et al. 2023

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“…Thus, novel screening tools are essential. A CT scan of the abdomen and pelvis is the standard imaging modality for staging and follow-up in patients with cancer [23], and radiologic assessments of the proximal femur are possible with CT-based digitally reconstructed radiographs [5,21].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer

Joo,

Ko,

Kim

et al. 2023

Clin Orthop Relat Res

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Background Improvement in survival in patients with advanced cancer is accompanied by an increased probability of bone metastasis and related pathologic fractures (especially in the proximal femur). The few systems proposed and used to diagnose impending fractures owing to metastasis and to ultimately prevent future fractures have practical limitations; thus, novel screening tools are essential. A CT scan of the abdomen and pelvis is a standard modality for staging and follow-up in patients with cancer, and radiologic assessments of the proximal femur are possible with CT-based digitally reconstructed radiographs. Deep-learning models, such as convolutional neural networks (CNNs), may be able to predict pathologic fractures from digitally reconstructed radiographs, but to our knowledge, they have not been tested for this application. Questions/purposes (1) How accurate is a CNN model for predicting a pathologic fracture in a proximal femur with metastasis using digitally reconstructed radiographs of the abdomen and pelvis CT images in patients with advanced cancer? (2) Do CNN models perform better than clinicians with varying backgrounds and experience levels in predicting a pathologic fracture on abdomen and pelvis CT images without any knowledge of the patients’ histories, except for metastasis in the proximal femur? Methods A total of 392 patients received radiation treatment of the proximal femur at three hospitals from January 2011 to December 2021. The patients had 2945 CT scans of the abdomen and pelvis for systemic evaluation and follow-up in relation to their primary cancer. In 33% of the CT scans (974), it was impossible to identify whether a pathologic fracture developed within 3 months after each CT image was acquired, and these were excluded. Finally, 1971 cases with a mean age of 59 ± 12 years were included in this study. Pathologic fractures developed within 3 months after CT in 3% (60 of 1971) of cases. A total of 47% (936 of 1971) were women. Sixty cases had an established pathologic fracture within 3 months after each CT scan, and another group of 1911 cases had no established pathologic fracture within 3 months after CT scan. The mean age of the cases in the former and latter groups was 64 ± 11 years and 59 ± 12 years, respectively, and 32% (19 of 60) and 53% (1016 of 1911) of cases, respectively, were female. Digitally reconstructed radiographs were generated with perspective projections of three-dimensional CT volumes onto two-dimensional planes. Then, 1557 images from one hospital were used for a training set. To verify that the deep-learning models could consistently operate even in hospitals with a different medical environment, 414 images from other hospitals were used for external validation. The number of images in the groups with and without a pathologic fracture within 3 months after each CT scan increased from 1911 to 22,932 and from 60 to 720, respectively, using data augmentation methods that are known to be an effective way to boost the performance of deep-learning models. Three CNNs (VGG16, ResNet50, and DenseNet121) were fine-tuned using digitally reconstructed radiographs. For performance measures, the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, precision, and F1 score were determined. The area under the receiver operating characteristic curve was used to evaluate three CNN models mainly, and the optimal accuracy, sensitivity, and specificity were calculated using the Youden J statistic. Accuracy refers to the proportion of fractures in the groups with and without a pathologic fracture within 3 months after each CT scan that were accurately predicted by the CNN model. Sensitivity and specificity represent the proportion of accurately predicted fractures among those with and without a pathologic fracture within 3 months after each CT scan, respectively. Precision is a measure of how few false-positives the model produces. The F1 score is a harmonic mean of sensitivity and precision, which have a tradeoff relationship. Gradient-weighted class activation mapping images were created to check whether the CNN model correctly focused on potential pathologic fracture regions. The CNN model with the best performance was compared with the performance of clinicians. Results DenseNet121 showed the best performance in identifying pathologic fractures; the area under the receiver operating characteristic curve for DenseNet121 was larger than those for VGG16 (0.77 ± 0.07 [95% CI 0.75 to 0.79] versus 0.71 ± 0.08 [95% CI 0.69 to 0.73]; p = 0.001) and ResNet50 (0.77 ± 0.07 [95% CI 0.75 to 0.79] versus 0.72 ± 0.09 [95% CI 0.69 to 0.74]; p = 0.001). Specifically, DenseNet121 scored the highest in sensitivity (0.22 ± 0.07 [95% CI 0.20 to 0.24]), precision (0.72 ± 0.19 [95% CI 0.67 to 0.77]), and F1 score (0.34 ± 0.10 [95% CI 0.31 to 0.37]), and it focused accurately on the region with the expected pathologic fracture. Further, DenseNet121 was less likely than clinicians to mispredict cases in which there was no pathologic fracture than cases in which there was a fracture; the performance of DenseNet121 was better than clinician performance in terms of specificity (0.98 ± 0.01 [95% CI 0.98 to 0.99] versus 0.86 ± 0.09 [95% CI 0.81 to 0.91]; p = 0.01), precision (0.72 ± 0.19 [95% CI 0.67 to 0.77] versus 0.11 ± 0.10 [95% CI 0.05 to 0.17]; p = 0.0001), and F1 score (0.34 ± 0.10 [95% CI 0.31 to 0.37] versus 0.17 ± 0.15 [95% CI 0.08 to 0.26]; p = 0.0001). Conclusion CNN models may be able to accurately predict impending pathologic fractures from digitally reconstructed radiographs of the abdomen and pelvis CT images that clinicians may not anticipate; this can assist medical, radiation, and orthopaedic oncologists clinically. To achieve better performance, ensemble-learning models using knowledge of the patients’ histories should be developed and validated. The code for our model is publicly available online at https://github.com/taehoonko/CNN_path_fx_prediction. Level of Evidence Level III, diagnostic study.

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Fast X-ray/CT image registration based on perspective projection triangular features

Sun,

Zhang,

Chen

et al. 2024

Computerized Medical Imaging and Graphics

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Assessment of Flatfoot Deformity Using Digitally Reconstructed Radiographs: Reliability and Comparison to Conventional Radiographs

Cited by 6 publications

References 32 publications

Axial Plane Rotation of the Talus in Progressive Collapsing Foot Deformity: A Weightbearing Computed Tomography Analysis

Axial Plane Rotation of the Talus in Progressive Collapsing Foot Deformity: A Weightbearing Computed Tomography Analysis

Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer

Fast X-ray/CT image registration based on perspective projection triangular features

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