A radiomics nomogram for predicting the meningioma grade based on enhanced T₁WI images

Abstract

Objectives: The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images. Methods: 188 patients with meningioma were analyzed retrospectively. There were 94 high grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer–Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and DCA curve. Results: The sex, size and surrounding invasion were used to build clinical model. The AUC of clinical model was 0.870 (95% CI: 0.782–0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802–0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904–1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p＜0.05). The DCA curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature. Conclusions: The radiomics nomogram based on enhanced T1WI images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma. Advances in knowledge: 1. We first constructed a radiomic nomogram to predict the meningioma grade. 2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study

“…In their study, seven models constructed by 2D features performed well with a high AUC (all>0.80), and SVM and KNN performed better than the other models with an AUC of 0.88 and a larger net benefit in the DCA curve. Duan C et al (10) first constructed a radiomics nomogram to predict meningioma grade with 2D features. Their radiomics nomogram had a good predictive…”

“…Radiomics has been used widely in recent years (7)(8)(9). Many researchers have applied radiomics in the study of meningioma, especially the prediction of meningioma grade (10)(11)(12)(13)(14)(15) (Table 1). In previous studies, there were two different methods applied to feature extraction: two-dimensional (2D) radiomics features (10,11) and three-dimensional (3D) radiomics features (12)(13)(14)(15).…”

“…Many researchers have applied radiomics in the study of meningioma, especially the prediction of meningioma grade (10)(11)(12)(13)(14)(15) (Table 1). In previous studies, there were two different methods applied to feature extraction: two-dimensional (2D) radiomics features (10,11) and three-dimensional (3D) radiomics features (12)(13)(14)(15). To the best of our knowledge, no study has directly compared the performance of 2D and 3D radiomics features in predicting meningioma grade.…”

Meningioma grading based on positron emission tomography: A systematic review and meta-analysis

“… 2 , 29 However, it should be noted that recent advances in the field of radiomics, combining MRI and machine learning, have shown promising applications for the imaging and grading of meningiomas. 30 , 31 , 32 This systematic review and meta-analysis aimed to comprehensively present the diagnostic accuracy of PET in distinguishing the meningioma grade driven from the current published evidence.…”

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

“…A nomogram can integrate multiple clinical factors and personalize the patient’s situation to accurately and effectively assess the causes and factors affecting the prognosis. [ 28 , 29 ]. Nomogram prediction models have been widely used in the prognosis and risk assessment of various tumors such as gastric, colorectal, prostate, breast, and liver cancers [ 30 , 31 , 32 , 33 , 34 ]; its visual interface and web-based model facilitate practical use by clinicians and patients, facilitating individualized patient treatment [ 35 ].…”