Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images - Phan, Nam Nhut; Huang, Chi-Cheng; Tseng, Ling-Ming; Chuang, Eric Y. (Frontiers in Oncology, 2021)
We proposed a highly versatile 2-steps 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. 4 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 with the aforementioned models. The 2-steps deep learning models showed promising classification results of the 4-breast cancer intrinsic subtypes with accuracy ranged from 0.68 (ResNet50) to 0.78 (ResNet101) in both testing and validation sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.914 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 shown 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.