Acta radiologica (Stockholm, Sweden : 1987) · 2024
Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.
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Wan Yuan, Miao Lei, Zhang HuanHuan, Wang YanMei, Li Xiao, Li Meng, Zhang Li
Аннотация
BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinically significant to concentrate on patients who have malignant tumors and differentiate between benign and malignant VCFs.
PURPOSE: To explore the value of multiple machine learning (ML) models based on CT radiomics features for differentiating benign and malignant VCFs in patients with malignant tumors.
MATERIAL AND METHODS: This study retrospectively analyzed 78 patients with malignant tumors accompanied by VCFs, 45 patients with benign VCFs, and 33 patients with malignant VCFs. A total of 140 lesions (86 benign lesions, 54 malignant lesions) were ultimately included in this study. All patients were divided into training sets (n = 98) and validation sets (n = 42) according to the 7:3 ratio. The radiomics features were screened and dimensioned, and multiple radiomics ML models were constructed. The receiver operating characteristic (ROC) curve was performed to assess the diagnostic performance.
RESULTS: Five radiomics features were included in the model. All the ML models built have good diagnostic efficiency, among which the support vector machine (SVM) model performs better. The area under the curve (AUC), sensitivity, specificity, and accuracy in the training set were 0.908, 0.816, 0.883, and 0.857, respectively, while those in the validation set were 0.911, 0.647, 0.92, and 0.81, respectively.
CONCLUSION: A variety of ML models built based on CT radiomics features have good value for differentiating benign and malignant VCFs in malignant tumor patients, and the SVM model has a better performance.
PURPOSE: To explore the value of multiple machine learning (ML) models based on CT radiomics features for differentiating benign and malignant VCFs in patients with malignant tumors.
MATERIAL AND METHODS: This study retrospectively analyzed 78 patients with malignant tumors accompanied by VCFs, 45 patients with benign VCFs, and 33 patients with malignant VCFs. A total of 140 lesions (86 benign lesions, 54 malignant lesions) were ultimately included in this study. All patients were divided into training sets (n = 98) and validation sets (n = 42) according to the 7:3 ratio. The radiomics features were screened and dimensioned, and multiple radiomics ML models were constructed. The receiver operating characteristic (ROC) curve was performed to assess the diagnostic performance.
RESULTS: Five radiomics features were included in the model. All the ML models built have good diagnostic efficiency, among which the support vector machine (SVM) model performs better. The area under the curve (AUC), sensitivity, specificity, and accuracy in the training set were 0.908, 0.816, 0.883, and 0.857, respectively, while those in the validation set were 0.911, 0.647, 0.92, and 0.81, respectively.
CONCLUSION: A variety of ML models built based on CT radiomics features have good value for differentiating benign and malignant VCFs in malignant tumor patients, and the SVM model has a better performance.