Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis

Zhiyuan Liu; Zekun Jiang; Li Meng; Jun Yang; Ying Liu; Yingying Zhang; Haiqin Peng; Jiahui Li; Gang Xiao; Zijian Zhang; Rongrong Zhou

doi:10.1155/2021/5518717

Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis

J Oncol. 2021 Jun 3:2021:5518717. doi: 10.1155/2021/5518717. eCollection 2021.

Authors

Zhiyuan Liu^{1

2}, Zekun Jiang³, Li Meng^{2

4}, Jun Yang⁵, Ying Liu⁶, Yingying Zhang^{1

2}, Haiqin Peng^{1

2}, Jiahui Li^{1

2}, Gang Xiao^{1

2}, Zijian Zhang^{1

2}, Rongrong Zhou^{1

2}

Affiliations

¹ Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China.
² Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China.
³ Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan 250358, Shandong, China.
⁴ Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China.
⁵ Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospita and Center, Kunming 650100, Yunnan, China.
⁶ Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.

Abstract

Objective: The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM).

Methods: A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods.

Results: In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; p < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities (p < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset).

Conclusion: Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.