Assist. Prof. Dr. Yuming Jiang | Computational Oncology | Best Researcher Award

Assistant Professor | Wake Forest University School of Medicine | United States

Dr. Yuming Jiang, MD, PhD, is a physician-scientist specializing in radiation oncology, artificial intelligence in cancer care, and precision oncology. His research integrates computational modeling, digital pathology, and radiomics to improve cancer diagnosis, prognosis prediction, and treatment response assessment. He has made pioneering contributions to the development of deep learning frameworks that noninvasively characterize the tumor microenvironment, predict immunotherapy response, and forecast recurrence and survival outcomes in gastrointestinal and other cancers. His high-impact publications in journals such as Nature Communications, The Lancet Digital Health, Annals of Oncology, and Journal of Clinical Oncology have significantly advanced the field of AI-driven oncology and personalized medicine. Dr. Jiang’s work emphasizes translational applications of biology-guided deep learning models to bridge clinical imaging, pathology, and genomics, offering novel insights into tumor biology and therapeutic decision-making. Beyond research, he actively contributes to the scientific community through editorial roles in leading journals including Frontiers in Oncology, Frontiers in Immunology, and npj Precision Oncology. With 2,944 citations across 2,326 documents, 78 publications, and an h-index of 29, Dr. Jiang’s scholarly impact reflects his leadership in computational oncology, fostering cross-disciplinary innovation between artificial intelligence, cancer biology, and clinical radiology to enhance patient outcomes and accelerate the integration of AI into precision cancer management.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

1. Wang, X., Jiang, Y., Yang, S., Wang, F., Zhang, X., Wang, W., Chen, Y., Wu, X., Xiang, J., Li, Y., Jiang, X., Yuan, W., Zhang, J., Yu, K., Ward, R., Hawkins, N., Jonnagaddala, J., Li, G., & Li, R. (2025). A foundation model for predicting prognosis and adjuvant therapy benefit from digital pathology in gastrointestinal cancers. Journal of Clinical Oncology, JCO-24-01501.

2. Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Yuan, Q., Xu, Y., Xing, L., Poultsides, G. A., Li, G., & Li, R. (2023). Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nature Communications, 14, 5135.

3. Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., Yuan, Q., Xi, S., Li, T., Xu, Y., Xiong, W., Wang, W., Li, G., & Li, R. (2023). Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Reports Medicine, 4, 101146.

4. Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., & Li, R. (2022). Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer using multi-task deep learning: A retrospective study. The Lancet Digital Health, 4(5), e340–e350. )

5. Jiang, Y., Li, R., & Li, G. (2023). Artificial intelligence for clinical oncology: Current status and future outlook. Science Bulletin, (23), 00113–5. R. (2023). Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning. Radiotherapy and Oncology, 186, 109793.

7. Huang, W., Jiang, Y. (co-first), Xiong, W., Sun, Z., Chen, C., Yuan, Q., Zhou, K., Han, Z., Hu, Y., Yu, J., Zhou, Z., Wang, W., Xu, Y., & Li, G. (2022). Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer. Nature Communications, 13, 5095.

8. Jiang, Y., Liang, X., Han, Z., Wang, W., Chen, C., Xu, Y., Zhou, Z., Poultsides, G. A., Li, G., & Li, R. (2021). Radiographical assessment of tumour stroma and treatment outcomes using deep learning: A retrospective, multicohort study. The Lancet Digital Health, 3(6), e371–e382.

9. Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Zhou, Z., Fisher, G. A. Jr., Li, G., & Li, R. (2021). Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: A multicenter, retrospective study. Annals of Surgery, 274(6), e1153–e1161.

10. Jiang, Y., Wang, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Zhou, Z., Xu, Y., Li, G., & Li, R. (2020). Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Annals of Oncology, 31(6), 760–768.