AUTHOR=Liu Dongming , Chen Jiu , Ge Honglin , Hu Xinhua , Yang Kun , Liu Yong , Hu Guanjie , Luo Bei , Yan Zhen , Song Kun , Xiao Chaoyong , Zou Yuanjie , Zhang Wenbin , Liu Hongyi 

TITLE=Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.848846

DOI=10.3389/fonc.2022.848846

ISSN=2234-943X

ABSTRACT=Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond the visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. Preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomics features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best AUC of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in binary classification diagnosis achieves the AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10 mm margin around the tumor) can provide additional information for the characterization of regional tumoural heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.