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.
Ming Cai;Xinjuan Fan;Zaiyi Liu;Ke Zhao;Lin Wu;Yanqi Huang;Minning Zhao;Qingru Hu;Qicong Chen;Su Yao;Zhenhui Li
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,ChinaDepartment of Pathology,The Sixth Affiliated Hospital of Sun Yat-sen University,Guangzhou,Guangdong 510655,ChinaGuangdong 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,ChinaDepartment of Pathology,The Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital,Yunnan Cancer Center,Kunming,Yunnan 650118,ChinaThe Second School of Clinical Medicine,Southern Medical University,Guangzhou,Guangdong 510515,ChinaInstitute of Computing Science and Technology,Guangzhou University,Guangzhou,Guangdong 510006,ChinaDepartment of Pathology,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou,Guangdong 510080,China
Colorectal cancer;Artificial intelligence;Deep learning;Digital pathology;Prognosis;Immune cells;CD3;CD8;TME
《中华医学杂志（英文版）》 2024 (004)
421-430 / 10
This work was supported by grants from the National Key R&D Program of China(No.2021YFF1201003),the National Science Fund for Distinguished Young Scholars(No.81925023),the Key-Area Research and Development Program of Guangdong Province(No.2021B0101420006),the Guangdong Provincial Key Lab-oratory of Artificial Intelligence in Medical Image Analysis and Application(No.2022B1212010011),the High-level Hospital Construction Project(No.DFJHBF202105),and the National Science Foundation for Young Scientists of China(No.82001986).
10.1097/CM9.0000000000002964
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