Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients

• Published in: Neuro-oncology (Charlottesville, Va.) Vol. 21; no. 1; pp. 115 - 124
• Main Authors: Dasgupta, Archya, Gupta, Tejpal, Pungavkar, Sona, Shirsat, Neelam, Epari, Sridhar, Chinnaswamy, Girish, Mahajan, Abhishek, Janu, Amit, Moiyadi, Aliasgar, Kannan, Sadhana, Krishnatry, Rahul, Sastri, Goda Jayant, Jalali, Rakesh
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 01.01.2019
• Subjects: Adolescent; Adult; Biomarkers, Tumor - genetics; Cerebellar Neoplasms - classification; Cerebellar Neoplasms - genetics; Cerebellar Neoplasms - pathology; Child; Child, Preschool; Female; Follow-Up Studies; Hedgehog Proteins - genetics; Humans; Male; Medulloblastoma - classification; Medulloblastoma - genetics; Medulloblastoma - pathology; Middle Aged; Multiparametric Magnetic Resonance Imaging - methods; Neuroimaging; Nomograms; Prognosis; Prospective Studies; Retrospective Studies; Young Adult; molecular; radiogenomics; nomogram; medulloblastoma; MRI
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noy093

Abstract Background Novel biological insights have led to consensus classification of medulloblastoma into 4 distinct molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We aimed to predict molecular subgrouping in medulloblastoma based on preoperative multiparametric magnetic resonance imaging (MRI) characteristics. Methods A set of 19 MRI features were evaluated in 111 patients with histologic diagnosis of medulloblastoma for prediction of molecular subgrouping. MRI characteristics were correlated with molecular subgroups derived from tissue samples in 111 patients (WNT = 17, SHH = 44, Group 3 = 27, and Group 4 = 23). Multinomial logistic regression of imaging parameters was performed on a training cohort (TC) of 76 patients, representing two-thirds of randomly selected patients from each of 4 molecular subgroups, to generate binary nomograms. Validation of these nomograms was performed on the remaining 35 patients as the validation cohort (VC). Results Medulloblastoma subgroups could be accurately predicted by preoperative MRI features in 74% of cases. Predictive accuracy was excellent for SHH (95%), acceptably high for Group 4 (78%), modest for Group 3 (56%) and worst for WNT (41%) subgroup medulloblastoma. SHH-specific nomogram was associated with excellent correlation between TC and VC, with area under the curve (AUC) of 0.939 and 0.991, respectively. AUC for Group 4 was acceptable at 0.851 and 0.788 in TC and VC, respectively; however, these values were consistently suboptimal in WNT and Group 3 medulloblastoma. Conclusion The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.