A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning
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Jialun Wu, Haichuan Zhang, Zeyu Gao, Xinrui Bao, Tieliang Gong, Chunbao Wang, Chen Li
2021
Abstract
Diagnostic pathology, which is the basis and gold standard of cancer
diagnosis, provides essential information on the prognosis of the disease and
vital evidence for clinical treatment. Tumor region detection, subtype and
grade classification are the fundamental diagnostic indicators for renal cell
carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis
is subjective, differences in observation and diagnosis between pathologists is
common in hospitals with inadequate diagnostic capacity. The main challenge for
developing deep learning based RCC diagnostic system is the lack of large-scale
datasets with precise annotations. In this work, we proposed a deep
learning-based framework for analyzing histopathological images of patients
with renal cell carcinoma, which has the potential to achieve pathologist-level
accuracy in diagnosis. A deep convolutional neural network (InceptionV3) was
trained on the high-quality annotated dataset of The Cancer Genome Atlas (TCGA)
whole-slide histopathological image for accurate tumor area detection,
classification of RCC subtypes, and ISUP grades classification of clear cell
carcinoma subtypes. These results suggest that our framework can help
pathologists in the detection of cancer region and classification of subtypes
and grades, which could be applied to any cancer type, providing auxiliary
diagnosis and promoting clinical consensus.
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