Distinguishing the subtypes of medulloblastoma

The study presented by Gutierrez et al., (2014) \cite{Rodriguez2014} extended their work to distinguish MB subtypes. When classifying MB subtypes, the ADC textural features provided high classification rates than histogram features, where the sum average and sum variance provided classification rates of 89%. The small number of medulloblastoma limits the impact of the subtype classification rates, which is acknowledged by the author.
Aside from DWI, location of tumour origin has been investigated in order to predict the MB subgroup. Perreault et al., (2014) \cite{Perreault2014} investigated 47 medulloblastoma, which were categorised into histological subgroups. Of the histological subgroups there were 31 classic, 10 large cell / anaplastic, 4 desmoplastic and 2 others. Of the molecular classification, there were 4 WNT, 13 SHH, 13 Group 2 and 17 Group 4. In this study, a multivariate logistic regression approach was used in order to identify significant predictors of MB subgroups. A validation cohort was then assessed which contained a further 52 MB, comprising of 37 classic MB, 4 large cell/anaplastic, 11 desmoplastic and 0 others within the histological classification, and 10 WNT, 11 SHH, 12 Group 3 and 19 Group 4 within the molecular classification. Results demonstrated that location, pattern of enhancement and tumour margin were significant predictors of MB subgroups, where 69% were accurately classified.
Zhao et al., (2017)  \cite{Zhao2017} investigated a cohort of 60 paediatric MB consisting of 8 WNTs, 17 SHH, 15 Group 3 and 20 Group 4 tumours. It was found that SHH, WNT and Group 4 significantly correlated with localisation pattern, and Group 4 MBs also demonstrated a significant association with minimal/no enhancement. Group 3 MB were not assessed for correlations with localisation patterns and other conventional MRI features. Despite demonstrating a promising model for predicting the molecular subgroup in adults, this same strategy was not applied for predicting paediatric cohort. Aside from a few differences between the SHH MBs, the localisation patterns were found to be similar between the paediatric and adult cohorts in the other subgroups.
Dasgupta et al., (2018) \cite{Dasgupta2018} assessed 111 medulloblastoma, which comprised of 17 WNT, 44 SHH, 27 Group 3 and 23 Group 4. Each tumour was qualitatively assessed using 19 different features describing characteristics such as tumour size, location and MR intensities. 76 cases were used as a training data set for multinomial logistic regression in order to create a model which could predict the molecular subgroup of a validation cohort. Validation demonstrated that accurate classification was achieved in 71% of cases; SHH and Group 4 achieved high classification accuracy (95% and 78%), but Group 3 and WNT achieved low classification accuracy (56% and 41%).

Summary

Despite the fact that most tumours displaying a high degree of laterality are of the WNT subtype, the