Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning

Ningrong Ye; Qi Yang; Ziyan Chen; Chubei Teng; Peikun Liu; Xi Liu; Yi Xiong; Xuelei Lin; Shouwei Li; Xuejun Li

doi:10.3389/fonc.2022.844197

Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning

Front Oncol. 2022 Mar 3:12:844197. doi: 10.3389/fonc.2022.844197. eCollection 2022.

Authors

Ningrong Ye^{1

2}, Qi Yang^{1

2}, Ziyan Chen^{1

2}, Chubei Teng^{1

2}, Peikun Liu^{1

2}, Xi Liu^{1

2}, Yi Xiong^{1

2}, Xuelei Lin^{1

2}, Shouwei Li³, Xuejun Li²

Affiliations

¹ Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
² Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China.
³ Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.

Abstract

Background: Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia.

Methods: This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization.

Results: The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors.

Conclusions: This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.

Keywords: deep neural network; germinoma; glioma; machine learning; transfer learning.