Radiogenomics prediction for MYCN amplification in Neuroblastoma: a
hypothesis generating study
Abstract
Background: MYCN amplification represents a powerful prognostic factor
in neuroblastoma (NB) and may occasionally account for intratumoral
heterogeneity. Radiomics is an emerging field of advanced image analysis
that aims to extract a large number of quantitative features from
standard radiological images, providing valuable clinical information
Procedure: In this retrospective study, we aimed to create a
radiogenomics model by correlating computed tomography (CT) radiomics
analysis with MYCN status and overall survival (OS). NB lesions were
segmented on pre-therapy CT scans and radiomics features subsequently
extracted using a dedicated library. Dimensionality reduction/features
selection approaches were then used for features procession and logistic
regression models have been developed for the considered outcome.
Results: Seventy-eight patients were included in this study, 24
presented MYCN amplification. In total, 232 radiomics features were
extracted. Eight features were selected through Boruta algorithm and 2
features were lastly chosen through Pearson correlation analysis: mean
of voxel intensity histogram (p=0.0082) and zone size non-uniformity
(p=0.038). Five-times repeated 3-fold cross-validation logistic
regression models yielded an Area Under the Curve (AUC) value of 0.879
on the training and 0.865 on the testing set for MYCN. No statistical
significant difference has been observed comparing radiomics predicted
and actual OS data. Conclusions: CT based radiomics is able to predict
MYCN amplification status and OS in NB, paving the way to the in depth
analysis of imaging based biomarkers that could enhance outcomes
prediction.