An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases

Abstract

BackgroundThe effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.MethodsOne hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC).ResultsThrough the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57–0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness.ConclusionsWe developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

“…All studies except one investigated the power of radiomic features arising from MRI for DDx. Only two investigated radiomic features from 18FDG-PET [ 23 , 24 ] and only one investigated the power of CT radiomics for glioma DDx [ 25 ]. A total of 20 studies focused on radiomics for DDx of primary nervous system lymphoma (PCNSL) and glioma (47.6%), with all but one involving IV glioma grade (GBM) patients.…”

“…A total of 16 studies explored the diagnostic feasibility of radiomic features for DDx of glioma and metastases. All but two of them extracted radiomic features from MRI sequences, while one evaluated features from CT [ 25 ] and one extracted features from PET [ 24 ]. In all but three studies [ 25 , 46 , 58 ], the glioma group consisted of patients with grade IV glioma (GBM).…”

“…Among studies involving other modalities than MRI, Zhang et al [ 24 ] found that an integrated radiomics model incorporating DWI and 18F-FDG PET improved the performance of differentiating GBM from solitary brain metastases. Promising performances (AUC = 0.992) were also obtained from models built using CT-based textural features to differentiate patients with high-grade gliomas from those with solitary brain metastases.…”

“…In particular, CE-T1WI sequence was the most investigated since it is the first-line MRI sequence for glioma assessment. Only two studies investigated the ability of PET radiomic features to differentiate gliomas from metastases [ 24 ] and glioma from PCNSL [ 23 ], and only one study was on CT [ 25 ].…”

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

“…The AUC was 0.96, and the sensitivity, specificity, and accuracy were 91%, 88%, and 89%, respectively. Zhang et al [11] extracted radiomics features from the MRI and 18 F-FDG PET/CT images of 100 patients with solitary brain tumors (50 GBMs and 50 BM) and used a random forest (RF) classifier to make the prediction. They found that the diagnostic performance of the combined MRI and PET/CT radiomics model (AUC = 0.98) was better than either single radiomics model.…”