论文标题
一项针对神经母细胞瘤患者CT扫描将Mycn-Gene扩增相关的试点研究
A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans
论文作者
论文摘要
神经母细胞瘤是婴儿中最常见的癌症之一，该疾病的初始诊断很困难。目前，通过对肿瘤样本的侵入性病理检查检测MYCN基因扩增（MNA）状态。这很耗时，可能对儿童产生隐藏的影响。为了解决这个问题，我们采用多个机器学习（ML）算法来预测MYCN基因扩增的存在或不存在。该数据集由23例神经母细胞瘤患者的回顾性CT图像组成。与以前的工作不同，我们开发了算法，而没有手动分段的原发性肿瘤，这是耗时且不实用的。取而代之的是，我们只需要中心点的坐标以及由亚科训练的儿科放射科医生给出的肿瘤切片数。具体而言，基于CNN的方法使用预训练的卷积神经网络，基于放射线的方法提取了放射线学特征。我们的结果表明，基于CNN的方法的表现优于基于放射线学的方法。
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.
