Liang Cher-Wei, Fang Pei-Wei, Huang Hsuan-Ying, Lo Chung-Ming
Department of Pathology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 243, Taiwan.
School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 242, Taiwan.
Cancers (Basel). 2021 Nov 18;13(22):5787. doi: 10.3390/cancers13225787.
Gastrointestinal stromal tumors (GIST) are common mesenchymal tumors, and their effective treatment depends upon the mutational subtype of the genes. We established deep convolutional neural network (DCNN) models to rapidly predict drug-sensitive mutation subtypes from images of pathological tissue. A total of 5153 pathological images of 365 different GISTs from three different laboratories were collected and divided into training and validation sets. A transfer learning mechanism based on DCNN was used with four different network architectures, to identify cases with drug-sensitive mutations. The accuracy ranged from 87% to 75%. Cross-institutional inconsistency, however, was observed. Using gray-scale images resulted in a 7% drop in accuracy (accuracy 80%, sensitivity 87%, specificity 73%). Using images containing only nuclei (accuracy 81%, sensitivity 87%, specificity 73%) or cytoplasm (accuracy 79%, sensitivity 88%, specificity 67%) produced 6% and 8% drops in accuracy rate, respectively, suggesting buffering effects across subcellular components in DCNN interpretation. The proposed DCNN model successfully inferred cases with drug-sensitive mutations with high accuracy. The contribution of image color and subcellular components was also revealed. These results will help to generate a cheaper and quicker screening method for tumor gene testing.
胃肠道间质瘤（GIST）是常见的间充质肿瘤，其有效治疗取决于基因的突变亚型。我们建立了深度卷积神经网络（DCNN）模型，以从病理组织图像中快速预测药物敏感突变亚型。收集了来自三个不同实验室的365种不同GIST的5153张病理图像，并将其分为训练集和验证集。基于DCNN的迁移学习机制与四种不同的网络架构一起使用，以识别具有药物敏感突变的病例。准确率在87%至75%之间。然而，观察到跨机构的不一致性。使用灰度图像导致准确率下降7%（准确率80%，灵敏度87%，特异性73%）。仅使用包含细胞核的图像（准确率81%，灵敏度87%，特异性73%）或细胞质的图像（准确率79%，灵敏度88%，特异性67%）分别导致准确率下降6%和8%，这表明在DCNN解释中跨亚细胞成分存在缓冲效应。所提出的DCNN模型成功地高精度推断出具有药物敏感突变的病例。还揭示了图像颜色和亚细胞成分的作用。这些结果将有助于生成一种更便宜、更快速的肿瘤基因检测筛选方法。
