Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer

The DLPS has the potential to enhance prognosis assessment and identify patients who are likely to benefit from adjuvant chemotherapy and immunotherapy in pan-gastrointestinal cancers, as well as in other solid tumors.

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Publication Details

Journal
Journal of Translational Medicine
Published
2025-10-14
DOI
https://doi.org/10.1186/s12967-025-06181-3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer

Yuming Jiang, Jingjing Xie, Zhenhui Li, M. Usman Ahmad et al.
Journal of Translational Medicine
Radiomics and Machine Learning in Medical Imaging
article

Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer

Yuming Jiang, Jingjing Xie, Zhenhui Li, M. Usman Ahmad, Guoxin Li, Wanying Feng, Xianqi Yang, Alyssa A. Guo, Zhe Li, Lin Wu, Jiang Yu, Yan Chen, Fan Yang, Md Tauhidul Islam, Zepang Sun, Wei Wang, T. Zhang
article en

Abstract

The DLPS has the potential to enhance prognosis assessment and identify patients who are likely to benefit from adjuvant chemotherapy and immunotherapy in pan-gastrointestinal cancers, as well as in other solid tumors.

Journal of Translational MedicineVol. 23(1)
Sun Yat-sen University (CN), Kunming Medical University (CN), Sun Yat-sen University Cancer Center (CN), Shenzhen Pingle Orthopedic Hospital (CN), Wake Forest University (US), Southern Medical University (CN), University of California, Davis (US), Stanford University (US)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Guangdong Province, Southern Medical University, Nanfang Hospital, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 57%
Radiomics and Machine Learning in Medical Imaging
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