Dataset Information

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

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 < 0.001). These results were confirmed in the internal and external validation groups with 5-year survival rates of low and high scores were 57.1% and 78.8%, 63.9% and 88.9%, respectively (internal: HR for high vs. low 0.49, 95% CI 0.28-0.88, P = 0.017; external: HR for high vs. low 0.35, 95% CI 0.15-0.83, P = 0.018). The combination of stroma-immune score and tumor-node-metastasis (TNM) stage showed better discrimination ability for survival prediction than using the TNM stage alone.

Conclusions

We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3⁺ and CD8⁺ T-cells densities within the stroma region on WSIs of CRC and further predict survival.

SUBMITTER: Xu Z

PROVIDER: S-EPMC8557607 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

Xu Zeyan Z Li Yong Y Wang Yingyi Y Zhang Shenyan S Huang Yanqi Y Yao Su S Han Chu C Pan Xipeng X Shi Zhenwei Z Mao Yun Y Xu Yao Y Huang Xiaomei X Lin Huan H Chen Xin X Liang Changhong C Li Zhenhui Z Zhao Ke K Zhang Qingling Q Liu Zaiyi Z

Cancer cell international 20211030 1

<h4>Background</h4>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 ...[more]

PMID: 34717647

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Project description:Background: It is urgent to develop robust prognostic biomarkers for non-metastatic colorectal cancer (CRC) patients undergoing surgery. The current study aimed to explore and compare the clinical significance of preoperative and postoperative blood tumor biomarkers including circulating tumor cells (CTCs), and develop prognostic models based on tumor biomarkers in patients with stage II-III CRC receiving surgery. Methods: A prospective study was performed to enroll 130 patients with stage II-III CRC receiving surgery between January 2015 and December 2017. Preoperative and postoperative blood tumor biomarkers including CTCs were detected and their prognostic value in predicting tumor recurrence-free survival (RFS) in stage II-III CRC were identified by Kaplan-Meier curves and Cox proportional hazard regression models. Results: CTCs counts within three postoperative days were significantly higher than preoperative CTCs (pre-CTCs). No significant association of pre-CTCs with clinical characteristics and tumor biomarkers was observed while positive postoperative CTCs (post-CTCs) were associated with female, older onset age, high TNM stage, tumor recurrence, and preoperative CEA. Kaplan-Meier curve with log-rank test and univariate Cox proportional hazard regression analysis suggested high N stage, TNM stage, positive pre-carbohydrate antigen (CA) 125, pre-CA19-9, post-CA125, post-CA19-9, post-CA72-4, post-carcinoembryonic antigen (CEA), and post-CTCs were correlated with poor RFS. In multivariate analysis, only TNM stage (adjusted HR=3.786, 95% CI=1.330-10.780; P=0.013), post-CA72-4 (adjusted HR=5.675, 95% CI=2.064-15.604; P=0.001), and post-CTCs (adjusted HR=2.739, 95% CI=1.042-7.200; P=0.041) were significantly correlated with poor RFS. We then developed prognostic models combining post-CTCs and post-CA72-4 with TNM stage or not to stratify the patients into different risk groups. These prognostic models exert a similar good performance in predicting tumor RFS in stage II-III CRC patients. Conclusions: Postoperative CTCs were prior to preoperative CTCs in predicting tumor recurrence survival in non-metastatic CRC patients undergoing surgery. We also developed CTCs-based prognostic models to predict tumor recurrence in stage II-III CRC, which might be used to identify the patients with high risk of recurrence and guide aggressive treatment to improve the clinical outcomes of those patients.

Dataset Information

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

Background

Methods

Results

Conclusions

Publications

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

Similar Datasets

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