https://scholars.lib.ntu.edu.tw/handle/123456789/607530
標題: | Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images | 作者: | Phan N.N Huang C.-C Tseng L.-M ERIC YAO-YU CHUANG |
關鍵字: | breast cancer intrinsic subtypes;convolutional neural networks;deep learning;PAM50;pathology;whole slide image;messenger RNA;area under the curve;Article;breast cancer;cancer recurrence;classification algorithm;gene expression;human;immunohistochemistry;nerve cell network;receiver operating characteristic;residual neural network;sensitivity and specificity;support vector machine;transfer of learning;triple negative breast cancer;tumor gene;whole genome sequencing | 公開日期: | 2021 | 卷: | 11 | 來源出版物: | Frontiers in Oncology | 摘要: | We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models. Copyright ? 2021 Phan, Huang, Tseng and Chuang. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121347777&doi=10.3389%2ffonc.2021.769447&partnerID=40&md5=a8046d3045bef0e96ca736e92f1c3e5a https://scholars.lib.ntu.edu.tw/handle/123456789/607530 |
ISSN: | 2234943X | DOI: | 10.3389/fonc.2021.769447 |
顯示於: | 生醫電子與資訊學研究所 |
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