Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

• Published in: Journal of neuro-oncology Vol. 140; no. 2; pp. 297 - 306
• Main Authors: Han, Yuqi, Xie, Zhen, Zang, Yali, Zhang, Shuaitong, Gu, Dongsheng, Zhou, Mu, Gevaert, Olivier, Wei, Jingwei, Li, Chao, Chen, Hongyan, Du, Jiang, Liu, Zhenyu, Dong, Di, Tian, Jie, Zhou, Dabiao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2018; Springer Nature B.V
• Subjects: Adolescent; Adult; Aged; Area Under Curve; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain Neoplasms - pathology; Chromosome 1; Chromosome Deletion; Chromosomes, Human, Pair 1; Chromosomes, Human, Pair 19; Clinical Study; Female; Gene deletion; Genotypes; Glioma; Glioma - diagnostic imaging; Glioma - genetics; Glioma - pathology; Humans; Image Interpretation, Computer-Assisted - methods; In Situ Hybridization, Fluorescence; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Grading; Neurology; Oncology; Radiomics; Retrospective Studies; ROC Curve; Young Adult; Lower-grade glioma; Magnetic resonance imaging; Prediction; 1p/19q Co-deletion; Radiomics
• ISSN: 0167-594X; 1573-7373
• DOI: 10.1007/s11060-018-2953-y

Purpose To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. Methods This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). Results The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. Conclusion Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.