Moitra Dipanjan, Mandal Rakesh Kr
University of North Bengal, Siliguri, West Bengal India.
Health Inf Sci Syst. 2019 Jul 30;7(1):14. doi: 10.1007/s13755-019-0077-1. eCollection 2019 Dec.
A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies.
The NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model.
The proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies.
The proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.
大部分肺癌属于非小细胞肺癌（NSCLC）。治疗方案规划和患者预后在很大程度上取决于美国癌症联合委员会（AJCC）分期等因素，AJCC分期是基于TNM分期的一种概括。到目前为止，人们在NSCLC自动分期方面已经做出了许多重大努力，但在该研究领域尚未观察到深度神经网络（DNN）的突破性应用。由于DNN使用了更深层的卷积神经网络（CNN），它能够比传统人工神经网络（ANN）实现更高的准确率。本研究的目的是提出一种简单而快速的结合循环神经网络（RNN）的CNN模型，用于NSCLC的自动AJCC分期，并将结果与一些标准机器学习算法以及一些类似研究进行比较。
本研究使用了癌症影像存档（TCIA）数据集中的NSCLC放射基因组学数据集。通过调整大小、增强、去噪等操作对肿瘤图像进行细化和滤波。在初始图像处理阶段之后进行基于纹理的图像分割。分割后的图像被输入到一个混合特征检测与提取模型中，该模型由两个连续阶段组成：最大稳定极值区域（MSER）和加速稳健特征（SURF）。经过长时间实验，得出了所需的CNN - RNN模型，并将提取的特征输入到该模型中。
所提出的CNN - RNN模型几乎优于所考虑的其他机器学习算法。其准确率一直高于其他同期研究。
所提出的CNN - RNN模型在研究中表现出色。可能需要进一步开展研究来优化该模型，并为肿瘤学家和放射科医生开发一个改进的辅助决策支持系统。
