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Diyi Zheng

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Proceedings Article | 27 November 2023 Poster + Paper

Diagnosis and survival prediction of colorectal cancer using deep learning from the histopathological HE images

Xiaolong Zhu, Shun Hu, Jingjing Wu, Yupeng Zhang, Diyi Zheng, Sheng Zhang, Hongqin Yang

Proceedings Volume 12767, 127671E (2023) https://doi.org/10.1117/12.2687606

KEYWORDS: Colorectal cancer, Tumors, Education and training, Tissues, Deep learning, Data modeling, Tumor growth modeling, Performance modeling, Error control coding, Statistical analysis

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In precision medicine, the diagnosis and prognosis of colorectal cancer (CRC) has always been a clinical focus, and subjective evaluation of histological slides by highly trained pathologists remains the gold standard in this field. With the innovation of deep learning in image analysis, convolutional neural networks (CNNs) can extract quantitative information from HE pathological images, and We found a certain correlation between this information and the progression of colorectal cancer. In this study, we first used a CNN model to classify HE pathological images. The CNN model was trained and validated using patches of 86 (from the NCT biobank and the UMM pathology archive) and 25 (from the DACHS study in the NCT biobank) colorectal HE pathological images, respectively. With this tool, we performed automated tissue decomposition of representative multitissue HE images from the The Cancer Genome Atlas (TCGA) cohort. Based on the output neuron activations in the CNN, we calculated the tumor-stroma-ratio (TSR). This score was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model. Finally, we validated these findings in an independent CRC dataset. Again, the score was an independent prognostic factor for OS. This study, deep learning methods can decompose complex tissue pathological images and extract prognostic factors from HE pathological images.

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