首页|Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis evaluation.The spatial distribution of CD3+and CD8+T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorec-tal cancer(CRC).This study aimed to investigate CD3CT(CD3+T cells density in the core of the tumor[CT])prognostic ability in patients with CRC by using AI technology.Methods:The study involved the enrollment of 492 patients from two distinct medical centers,with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort.To facilitate tissue segmentation and T-cells quantification in whole-slide images(WSIs),a fully automated workflow based on deep learning was devised.Upon the completion of tissue segmentation and subsequent cell segmentation,a comprehensive analysis was conducted.Results:The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3CT and CD3-CD8(the combination of CD3+and CD8+T cells density within the CT and invasive margin)in predicting mortality(C-index in training cohort:0.65 vs.0.64;validation cohort:0.69 vs.0.69).The CD3CT was confirmed as an independent prognostic factor,with high CD3CT density associated with increased overall survival(OS)in the training cohort(hazard ratio[HR]=0.22,95％confidence interval[CI]:0.12-0.38,P＜0.001)and validation cohort(HR=0.21,95％CI:0.05-0.92,P=0.037).Conclusions:We quantify the spatial distribution of CD3+and CD8+T cells within tissue regions in WSIs using AI technology.The CD3CT confirmed as a stage-independent predictor for OS in CRC patients.Moreover,CD3CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

Colorectal cancerArtificial intelligenceDeep learningDigital pathologyPrognosisImmune cellsCD3CD8TME

Ming Cai、Ke Zhao、Lin Wu、Yanqi Huang、Minning Zhao、Qingru Hu、Qicong Chen、Su Yao、Zhenhui Li、Xinjuan Fan、Zaiyi Liu

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Department of Radiology,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou,Guangdong 510080,China

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application,Guangzhou,Guangdong 510080,China

Guangdong Cardiovascular Institute,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou,Guangdong 510080,China

Medical Research Institute,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou,Guangdong 510080,China

Department of Pathology,The Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital,Yunnan Cancer Center,Kunming,Yunnan 650118,China

The Second School of Clinical Medicine,Southern Medical University,Guangzhou,Guangdong 510515,China

Institute of Computing Science and Technology,Guangzhou University,Guangzhou,Guangdong 510006,China

Department of Pathology,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou,Guangdong 510080,China

Department of Pathology,The Sixth Affiliated Hospital of Sun Yat-sen University,Guangzhou,Guangdong 510655,China

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National Key R&D Program of ChinaNational Science Fund for Distinguished Young ScholarsKey-Area Research and Development Program of Guangdong ProvinceGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationHighlevel Hospital Construction ProjectNational Science Foundation for Young Scientists of China

2021YFF1201003819250232021B01014200062022B1212010011DFJHBF20210582001986

2024

10.1097/CM9.0000000000002964

中华医学杂志(英文版)
中华医学会

中华医学杂志(英文版)

CSTPCD
影响因子：0.838
ISSN：0366-6999
年,卷(期)：2024.137(4)
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