Archya Dasgupta1, Tejpal Gupta1, Sona Pungavkar2, Neelam Shirsat3, Sridhar Epari4, Girish Chinnaswamy5, Abhishek Mahajan6, Amit Janu6, Aliasgar Moiyadi7, Sadhana Kannan8, Rahul Krishnatry1, Goda Jayant Sastri1, Rakesh Jalali1. 1. Department of Radiation Oncology, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 2. Department of Radiodiagnosis & Imaging, Global Hospitals, Mumbai, India. 3. Neuro-Oncology Lab, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 4. Department of Pathology, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 5. Department of Pediatric Oncology, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 6. Department of Radiodiagnosis, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 7. Department of Neurosurgical Oncology, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India. 8. Department of Clinical Trials Unit-Clinical Research Secretariat, Tata Memorial Hospital/Advanced Centre for Treatment, Research, & Education in Cancer, Tata Memorial Centre, Mumbai, India.
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.
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.
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