A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans

TL;DR

This study explores machine learning techniques, including CNNs and radiomics, to non-invasively predict MYCN gene amplification in neuroblastoma patients using CT scans, avoiding invasive procedures.

Contribution

It introduces a novel approach that does not require manual tumor segmentation, relying instead on simple tumor location data for prediction.

Findings

CNN-based method outperforms radiomics-based method

Non-invasive prediction of MYCN amplification achieved

Utilizes minimal tumor localization data

Abstract

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.