Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study

Huifen Ye^#^{1

2

3}, Ke Zhao^#^{3

4

5

6}, Yanfen Cui^#⁷, Zhenhui Li^#⁸, Huan Zhang^#⁹, Min-Er Zhong^#¹⁰, Chuanwen Fan⁹, Haitao Huang^{3

4

11}, Nicholas J Hawkins¹², Robyn L Ward^{12

13}, Xiao-Feng Sun⁹, Jinming Song¹⁴, Zaiyi Liu^{15

16}, Jitendra Jonnagaddala¹⁷, Tong Tong^{18

19}, Su Yao^{20

21

22}

Affiliations

¹ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
⁴ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
⁵ Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
⁶ Chinese Medicine Guangdong Laboratory, Hengqin, China.
⁷ Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
⁸ Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
⁹ Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
¹⁰ Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
¹¹ School of Medicine South China University of Technology, Guangzhou, China.
¹² School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia.
¹³ Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
¹⁴ Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
¹⁵ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. liuzaiyi@gdph.org.cn.
¹⁶ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China. liuzaiyi@gdph.org.cn.
¹⁷ School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia. Jitendra.Jonnagaddala@unsw.edu.au.
¹⁸ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. t983352@126.com.
¹⁹ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. t983352@126.com.
²⁰ Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. yaosu@gdph.org.cn.
²¹ Department of Pathology, Heyuan People's Hospital, Heyuan, China. yaosu@gdph.org.cn.
²² Heyuan Key Laboratory of Molecular Diagnosis & Disease Prevention and Treatment, Heyuan People's Hospital, Heyuan, China. yaosu@gdph.org.cn.

^# Contributed equally.

PMID: 41580788
DOI: 10.1186/s12967-026-07681-6

Free article

Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study

Huifen Ye et al. J Transl Med. 2026.

Free article

. 2026 Jan 24.

doi: 10.1186/s12967-026-07681-6. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
⁴ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
⁵ Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
⁶ Chinese Medicine Guangdong Laboratory, Hengqin, China.
⁷ Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
⁸ Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
⁹ Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
¹⁰ Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
¹¹ School of Medicine South China University of Technology, Guangzhou, China.
¹² School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia.
¹³ Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
¹⁴ Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
¹⁵ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. liuzaiyi@gdph.org.cn.
¹⁶ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China. liuzaiyi@gdph.org.cn.
¹⁷ School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia. Jitendra.Jonnagaddala@unsw.edu.au.
¹⁸ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. t983352@126.com.
¹⁹ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. t983352@126.com.
²⁰ Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. yaosu@gdph.org.cn.
²¹ Department of Pathology, Heyuan People's Hospital, Heyuan, China. yaosu@gdph.org.cn.
²² Heyuan Key Laboratory of Molecular Diagnosis & Disease Prevention and Treatment, Heyuan People's Hospital, Heyuan, China. yaosu@gdph.org.cn.

^# Contributed equally.

PMID: 41580788
DOI: 10.1186/s12967-026-07681-6

No abstract available

Keywords: Colorectal cancer; Deep learning; Immune; Tumor-stroma ratio; Whole-slide images.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committees of Guangdong Provincial People’s Hospital (KY2023-046–01), with the need for informed consent waived for this retrospective study. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.

References

1. Chen Y, Liang Z, Lai M. Targeting the devil: strategies against cancer-associated fibroblasts in colorectal cancer. Transl Res. 2024 Aug 1;270:81–93.
1. Wang W, Kandimalla R, Huang H, Zhu L, Li Y, Gao F, et al. Molecular subtyping of colorectal cancer: recent progress, new challenges and emerging opportunities. Semin Cancer Biol. 2019 Apr;55:37–52.
1. Hu JL, Wang W, Lan XL, Zeng ZC, Liang YS, Yan YR, et al. Cafs secreted exosomes promote metastasis and chemotherapy resistance by enhancing cell stemness and epithelial-mesenchymal transition in colorectal cancer. Mol Cancer. 2019 May 7;18:91.
1. de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023 Mar 13;41(3):374–403.
1. Cheng B, Yu Q, Wang W. Intimate communications within the tumor microenvironment: stromal factors function as an orchestra. J Biomed Sci. 2023 Jan 4;30(1):1.

LinkOut - more resources

Full Text Sources
- BioMed Central

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study

Affiliations

Artificial intelligence-based tumor-stroma ratio quantification reveals prognostic value and stromal-driven immunosuppression in colorectal cancer: an international validation study

Authors

Affiliations

Conflict of interest statement

References

LinkOut - more resources

Full Text Sources