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Computed tomography-based structural analysis for predicting fracture risk in children with benign skeletal neoplasms: comparison of specificity with that of plain radiographs.

ABSTRACT: The decision whether to treat benign skeletal lesions surgically can be difficult to make. The purpose of this study was to validate our previously published method of predicting fracture risk with use of quantitative computed tomography-based structural analysis.We prospectively studied a group of children who presented to a major children's hospital with a benign appendicular skeletal lesion between 2002 and 2007. As in our previous study, the resistance of the affected bone to compressive, bending, and torsional loads was calculated with rigidity analysis performed with the use of serial transaxial quantitative computed tomography data obtained along the length of the bone containing the lesion and from homologous cross sections through the contralateral, normal bone. At each cross section, the ratio of the structural rigidity of the affected bone to that of the normal, contralateral bone was determined.Forty-one patients who had not received surgical treatment for the skeletal lesion met the criteria for our study. Thirty-four (83%) of these individuals completed our activity questionnaire at least two years after the quantitative computed tomography-based rigidity analysis. None of the patients for whom no increased fracture risk had been predicted by the rigidity analysis sustained a fracture, even though they had not received surgical treatment.Many considerations other than the predicted fracture risk are factored into the decision of whether to treat a benign skeletal lesion. However, this study indicated that quantitative computed tomography-based rigidity analysis is more specific (97% specificity) than criteria based on plain radiographs (12% specificity) for predicting the risk of a pathologic fracture since fracture risk indices based on lesion size alone fail to account for the compensatory remodeling of the host bone that occurs in response to the presence of the lesion in a growing child.Prognostic Level I. See Instructions to Authors for a complete description of levels of evidence.

SUBMITTER: Leong NL

PROVIDER: S-EPMC2909715 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Computed tomography-based structural analysis for predicting fracture risk in children with benign skeletal neoplasms: comparison of specificity with that of plain radiographs.

Leong Natalie L NL Anderson Megan E ME Gebhardt Mark C MC Snyder Brian D BD

The Journal of bone and joint surgery. American volume 20100801 9

<h4>Background</h4>The decision whether to treat benign skeletal lesions surgically can be difficult to make. The purpose of this study was to validate our previously published method of predicting fracture risk with use of quantitative computed tomography-based structural analysis.<h4>Methods</h4>We prospectively studied a group of children who presented to a major children's hospital with a benign appendicular skeletal lesion between 2002 and 2007. As in our previous study, the resistance of t ...[more]

PMID: 20686056

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Project description:Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.

Dataset Information

Computed tomography-based structural analysis for predicting fracture risk in children with benign skeletal neoplasms: comparison of specificity with that of plain radiographs.

Publications

Computed tomography-based structural analysis for predicting fracture risk in children with benign skeletal neoplasms: comparison of specificity with that of plain radiographs.

Similar Datasets

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