Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis.
10.1097/CM9.0000000000002964
- Author:
Ming CAI
1
;
Ke ZHAO
2
;
Lin WU
3
;
Yanqi HUANG
1
;
Minning ZHAO
4
;
Qingru HU
4
;
Qicong CHEN
5
;
Su YAO
6
;
Zhenhui LI
3
;
Xinjuan FAN
7
;
Zaiyi LIU
1
Author Information
1. Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China.
2. Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China.
3. Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China.
4. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China.
5. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China.
6. Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China.
7. Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Lymphocytes, Tumor-Infiltrating;
Colorectal Neoplasms;
Artificial Intelligence;
Reproducibility of Results;
Prognosis;
CD8-Positive T-Lymphocytes;
Tumor Microenvironment
- From:
Chinese Medical Journal
2024;137(4):421-430
- CountryChina
- Language:English
-
Abstract:
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 colorectal cancer (CRC). This study aimed to investigate CD3 CT (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 CD3 CT 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 CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT 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 CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
