| Autor: |
Zeyan Xu, Yong Li, Yingyi Wang, Shenyan Zhang, Yanqi Huang, Su Yao, Chu Han, Xipeng Pan, Zhenwei Shi, Yun Mao, Yao Xu, Xiaomei Huang, Huan Lin, Xin Chen, Changhong Liang, Zhenhui Li, Ke Zhao, Qingling Zhang, Zaiyi Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Cancer Cell International, Vol 21, Iss 1, Pp 1-12 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1475-2867 |
| DOI: |
10.1186/s12935-021-02297-w |
| Popis: |
Abstract Background Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC. Methods Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3+ and CD8+ T-cells infiltration in the stroma region. Results Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24–0.63, P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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