Clinical neurology and neurosurgery · 2021
Tumor volume and the dural tail sign enable the differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from high-grade meningioma.
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Xiao Dongdong, Shi Jiawei, Zhou Mengting, Yan Ling, Zhao Zhen, Hu Tingting, Guo Xuebing, Zhao Hongyang, Yan Pengfei, Jiang Xiaobing
Аннотация
OBJECTIVES: Intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) is a rare mesenchymal neoplasm with imaging features mimicking high-grade meningioma (HGM) and can easily be misdiagnosed. We sought to determine the value of routine preoperative data in differentiating these tumors.
PATIENTS AND METHODS: Patients with confirmed SFT/HPC or HGM between January 2012 and June 2020 were identified. A total of 28 preoperative variables (including age, sex, tumor location, tumor volume, 10 traditional MRI features, and 14 peripheral blood indices) were collected for each patient. The top features were selected sequentially based on the least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE) methods. Differentiation and calibration of the classifiers were assessed by receiver operating characteristic (ROC) curves and calibration curves, respectively. Nomograms were constructed based on multivariate analysis.
RESULTS: A total of 127 patients, including 29 with SFT/HPC and 98 with HGM, were analyzed. Three features were first selected using the LASSO and SVM-RFE methods, and corresponding models were developed. Although the area under the curve (AUC) of model 1 was the highest, a comprehensive analysis suggested the superiority of model 2, which consisted only of the features tumor volume (TV) and dural tail sign (DTS) (AUC: 0.942, sensitivity: 93.10%, p-value of H-L test: 0.734, Brier score: 0.07). A risk score formula and a nomogram were constructed.
CONCLUSIONS: TV can be used to effectively identify SFT/HPC and HGM, whereas adding DTS can improve the overall prediction accuracy. As these two variables are routinely available and are easy for clinicians to master, they can provide a powerful reference for clinical decision-making.
PATIENTS AND METHODS: Patients with confirmed SFT/HPC or HGM between January 2012 and June 2020 were identified. A total of 28 preoperative variables (including age, sex, tumor location, tumor volume, 10 traditional MRI features, and 14 peripheral blood indices) were collected for each patient. The top features were selected sequentially based on the least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE) methods. Differentiation and calibration of the classifiers were assessed by receiver operating characteristic (ROC) curves and calibration curves, respectively. Nomograms were constructed based on multivariate analysis.
RESULTS: A total of 127 patients, including 29 with SFT/HPC and 98 with HGM, were analyzed. Three features were first selected using the LASSO and SVM-RFE methods, and corresponding models were developed. Although the area under the curve (AUC) of model 1 was the highest, a comprehensive analysis suggested the superiority of model 2, which consisted only of the features tumor volume (TV) and dural tail sign (DTS) (AUC: 0.942, sensitivity: 93.10%, p-value of H-L test: 0.734, Brier score: 0.07). A risk score formula and a nomogram were constructed.
CONCLUSIONS: TV can be used to effectively identify SFT/HPC and HGM, whereas adding DTS can improve the overall prediction accuracy. As these two variables are routinely available and are easy for clinicians to master, they can provide a powerful reference for clinical decision-making.