An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer

Autor: Qicong Chen, Ming Cai, Xinjuan Fan, Wenbin Liu, Gang Fang, Su Yao, Yao Xu, Qian Li, Yingnan Zhao, Ke Zhao, Zaiyi Liu, Zhihua Chen
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: BMC Cancer, Vol 23, Iss 1, Pp 1-10 (2023)
Druh dokumentu: article
ISSN: 1471-2407
DOI: 10.1186/s12885-023-11289-0
Popis: Abstract Background and objective In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. Materials and methods In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). Results The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27–0.77, P = 0.0014) and validation cohort (0.21, 0.10–0.46,
Databáze: Directory of Open Access Journals
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