Differentiation between Germinoma and Craniopharyngioma Using Radiomics-Based Machine Learning

• Published in: Journal of personalized medicine Vol. 12; no. 1; p. 45
• Main Authors: Chen, Boran, Chen, Chaoyue, Zhang, Yang, Huang, Zhouyang, Wang, Haoran, Li, Ruoyu, Xu, Jianguo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.01.2022; MDPI
• Subjects: Accuracy; Algorithms; Brain cancer; Chemotherapy; Differential diagnosis; Discriminant analysis; Feature selection; Image processing; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical records; Neoplasia; Patients; Pituitary; Precision medicine; Radiation therapy; Radiomics; Support vector machines; Tumors; radiomics; magnetic resonance imaging; germinoma; machine learning; craniopharyngioma; texture analysis
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm12010045

For the tumors located in the anterior skull base, germinoma and craniopharyngioma (CP) are unusual types with similar clinical manifestations and imaging features. The difference in treatment strategies and outcomes of patients highlights the importance of making an accurate preoperative diagnosis. This retrospective study enrolled 107 patients diagnosed with germinoma ( = 44) and CP ( = 63). The region of interest (ROI) was drawn independently by two researchers. Radiomic features were extracted from contrast-enhanced T1WI and T2WI sequences. Here, we established the diagnosis models with a combination of three selection methods, as well as three classifiers. After training the models, their performances were evaluated on the independent validation cohort and compared based on the index of the area under the receiver operating characteristic curve (AUC) in the validation cohort. Nine models were established and compared to find the optimal one defined with the highest AUC in the validation cohort. For the models applied in the contrast-enhanced T1WI images, RF + RF and LASSO + LDA were observed to be the optimal models with AUCs of 0.91. For the models applied in the T2WI images, DC + LDA and LASSO + LDA were observed to be the optimal models with AUCs of 0.88. The evidence of this study indicated that radiomics-based machine learning could be potentially considered as the radiological method in the presurgical differential diagnosis of germinoma and CP with a reliable diagnostic performance.