Neuroblastoma is one of the most common cancers in infants, and the initial
diagnosis of this disease is difficult. At present, the MYCN gene amplification
(MNA) status is detected by invasive pathological examination of tumor samples.
This is time-consuming and may have a hidden impact on children. To handle this
problem, we adopt multiple machine learning (ML) algorithms to predict the
presence or absence of MYCN gene amplification. The dataset is composed of
retrospective CT images of 23 neuroblastoma patients. Different from previous
work, we develop the algorithm without manually-segmented primary tumors which
is time-consuming and not practical. Instead, we only need the coordinate of
the center point and the number of tumor slices given by a subspecialty-trained
pediatric radiologist. Specifically, CNN-based method uses pre-trained
convolutional neural network, and radiomics-based method extracts radiomics
features. Our results show that CNN-based method outperforms the
radiomics-based method