Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics

Duan, Shuo; Hua, Yichun; Cao, Guanmei; Hu, Jun‐Nan; Cui, Wei; Zhang, D.F.; Xu, Sheng; Rong, Tianhua; Liu, Baoge

doi:10.1016/j.ejrad.2023.110899

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…In addition, the intravertebral vacuum phenomenon has never been visualized in malignant VCFs, although its occurrence is uncommon and not statistically significant [ 1 ]. CT findings that are predictive of malignant VCFs include osteolytic destruction and epidural or focal paravertebral soft tissue masses [ 24 , 25 ]. One study showed an accuracy of 89.7% in the differentiation of malignant from osteoporotic vertebral fractures based on the CT scoring system [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies have shown that radiomics and deep learning based on CT and MRI have good diagnostic performance in distinguishing benign and malignant VCFs. The AUCs of the radiomics score on CT for predicting the malignancy probability of VCFs were 0.852–0.97 [ 19 , 25 ]. The AUC and accuracy of machine learning based on MRI to identify benign versus malignant indistinguishable VCFs were 0.86 and 87.61%, respectively [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

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Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

Wang,

Zhou,

Kong

et al. 2023

EJNMMI Res

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Background Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). Results Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical–radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical–radiomics model (p > 0.05). The accuracies of the radiomics and clinical–radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer–Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical–radiomics model exhibited higher overall net benefit than the clinical model. Conclusions The PET/CT-based radiomics and clinical–radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

Wang,

Zhou,

Kong

et al. 2023

EJNMMI Res

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“…DL utilizes deep convolutional neural networks (CNN) to automatically extract high-dimensional features from CT images, enabling end-to-end learning without requiring manual feature extraction [13,14]. DL has exhibited remarkable performance in image analysis and has proven advantages in differentiating between benign and malignant vertebral compression fractures [15]. Although both Rad and DL methods have demonstrated promising diagnostic capabilities in relevant aspects, there exists a dearth of studies comparing their performance in BMD assessment based on chest LDCT images, especially 80 kVp CT images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images

Tong,

Wang,

Zhang

et al. 2024

Bioengineering

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Objective: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. Methods: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.

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Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

Yu,

Liu

et al. 2024

European Journal of Radiology

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Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics

Cited by 14 publications

References 23 publications

Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

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