Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
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DOI：
10.3390/app11010254
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发表时间：
2021-01-01
影响因子：
2.7
通讯作者：
Hong, Ayoung
中科院分区：
文献类型：
--
作者：
Lee, Hyunseok;Seo, Jihyun;Hong, Ayoung
结直肠癌是最常见的癌症之一，死亡率高。确定切除的肿瘤组织中微卫星不稳定性(MSI)状态是至关重要的，因为它有助于诊断相关疾病和确定相关的治疗方法。提出了一种基于深度学习的MSI状态预测的两阶段分类方法。所提出的流水线包括分段网络和分类网络的串联。在第一阶段，使用特征金字塔网络(FPN)从给定的病理图像中分割出肿瘤区域。在第二阶段，分割的肿瘤被划分为MSI-L或MSI-H。我们使用放大倍数为10倍和20倍的病理图像检验了所提出的方法的性能，并与在一个阶段识别组织类型的传统多类分类方法进行了比较。在放大倍数为10倍和20倍的情况下，该方法的F1得分均高于传统方法。此外，我们还验证了放大20倍的F1分数要好于放大10倍的F1分数。
Colorectal cancer is one of the most common cancers with a high mortality rate. The determination of microsatellite instability (MSI) status in resected cancer tissue is vital because it helps diagnose the related disease and determine the relevant treatment. This paper presents a two-stage classification method for predicting the MSI status based on a deep learning approach. The proposed pipeline includes the serial connection of the segmentation network and the classification network. In the first stage, the tumor area is segmented from the given pathological image using the Feature Pyramid Network (FPN). In the second stage, the segmented tumor is classified as MSI-L or MSI-H using Inception-Resnet-V2. We examined the performance of the proposed method using pathological images with 10x and 20x magnifications, in comparison with that of the conventional multiclass classification method where the tissue type is identified in one stage. The F1-score of the proposed method was higher than that of the conventional method at both 10x and 20x magnifications. Furthermore, we verified that the F1-score for 20x magnification was better than that for 10x magnification.