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

doi:10.1117/12.2687606

27 November 2023 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

Author Affiliations +

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 127671E (2023) https://doi.org/10.1117/12.2687606
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaolong Zhu, Shun Hu, Jingjing Wu, Yupeng Zhang, Diyi Zheng, Sheng Zhang, and Hongqin Yang "Diagnosis and survival prediction of colorectal cancer using deep learning from the histopathological HE images", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 127671E (27 November 2023); https://doi.org/10.1117/12.2687606

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