Selected Publications

Jiang Y, Zhang Z, Wang W. ... Li RBiology-guided deep learning predicts prognosis and cancer immunotherapy responseNature Communications 14, 5135 (2023). 

Jiang Y, Zhou K, . . . Li R. Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Reports Medicine, 2023; 4(8), 101146

Jiang Y, Li R*, Li G*. Artificial intelligence for clinical oncology: current status and future outlookScience Bulletin. 2023. *Corresponding authors

Li, Z., Jiang, Y., Li, B., Han, Z., Shen, J., Xia, Y., Li, R. Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal CancersJAMA Network Open2023; 6 (1): e2252553

Jiang Y, ... Li RPredicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. Lancet Digital Health. 2022; 4 (5): e340-e350

This article is highlighted in an accompanying Comment published in the same issue of the Lancet Digital Health.

This work showcases the human-AI collaboration, a promising direction in medical AI applications

Wu J, Li C, Gensheimer MF, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW Jr, Wakelee H, Diehn M, and Li R.  Radiological tumor classification across imaging modality and histologyNature Machine Intelligence, 3, pages 787–798 (2021). 

This work is highlighted in a News and Views article published in the same journal. See also report by Stanford Institute for Human-Centered Artificial Intelligence.

'machine intelligence enables radiomics strategies that provide more biologically interpretable predictions — a property of high clinical significance.'

Jiang, Y., Liang, X., Han, Z., Wang, W., Xie, Y., Xu, Y., Zhou, Z., Poultsides G. A., Li, G., Li, R. Radiographic assessment of tumor stroma and treatment outcomes using deep learning: a retrospective multicohort study. Lancet Digital Health2021; 3 (6): e371-e382


This is the first study on using deep learning to radiographically assess the tumor stromal microenvironment and predict treatment outcomes.

Jin C, Yu H, Ke J, Ding P, Yi Y, Jiang X, Duan X, Tang J, Chang DT, Wu X, Gao F, Li RPredicting treatment response from longitudinal images using multi-task deep learningNature Communications. 2021; 12: 1851

This work pioneers a deep learning approach to analyze longitudinal medical images for treatment response prediction.

Jiang Y, Wang H, Wu J, Chen C, Yuan Q, Huang W, Li T, Xi S, Hu Y, Zhou Z, Xu Y, Li G, and Li RNoninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancerAnnals of Oncology. 2020 Jun;31(6):760-768

This work established a noninvasive approach to evaluate the immune microenvironment from radiologic images.

Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Hu, Y., Xu, Y., Zhou, Z., Fisher, G. A., Li, G., Li, R. Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective StudyAnnals of Surgery2020

This work was reported by Reuters Health in January 2020.

Deep-learning analysis of diagnostic CT scans can reveal subtle image features that reflect underlying disease biology related to tumor progression and treatment response.


  1. Li B, Jiang Y, Li G, Fisher GA Jr, and Li RNatural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefitJCI Insight. 2020 
  2. Wu J, Gensheimer MF, Zhang N, Guo M, Liang R, Zhang C, Fischbein N, Pollom EL, Beadle B, Le QT, and Li RTumor subregion evolution-based imaging features to assess early response and predict prognosis in oropharyngeal cancer. J Nucl Med, 2020 Mar; 61(3):327-336
  3. Wang H, Jiang Y, Li B, Cui Y, Li D, and Li RSingle-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma SubtypesCancers, 2020 Nov 28;12(12):3562


  1. Li B, Cui Y, Nambiar D, Sunwoo J, and Li RThe immune subtypes and landscape of squamous cell carcinomaClin Cancer Res. 2019;25:3528–37. Featured in Highlights of This Issue
  2. Wu J, Gensheimer MF, Zhang N, Han F, Liang R, Qian Y, Zhang C, Fischbein N, Pollom EL, Beadle B, Le QT, and Li RIntegrating tumor and nodal imaging characteristics at baseline and mid-treatment CT scans to predict distant metastasis in oropharyngeal cancer treated with concurrent chemoradiotherapy. Int J Radiat Oncol Biol Phys, 2019 Jul 15;104(4):942-952


  1. Cui Y, Li B, Pollom EL, Horst KC, Li RIntegrating radiosensitivity and immune gene signatures for predicting benefit of radiotherapy in breast cancerClin Cancer Res. 2018 Oct 1;24(19):4754-4762. Featured in Highlights of This Issue
  2. Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, Kurian AW, Daniel BL, and Li RIntratumoral spatial heterogeneity by perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapyRadiology, 2018 Jul;288(1):26-35; PMID: 29714680. Highlighted by an Editorial
  3. Lee J, Li B, Cui Y, Sun X, Wu J, Zhu H, Yu J, Gensheimer MF, Loo BW Jr, Diehn M, and Li RA quantitative CT imaging signature predicts survival and complements established prognosticators in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1098-1106.


  1. Li B, Cui Y, Diehn M, Li R. Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non-Small Cell Lung Cancer. JAMA Oncol. 2017 Nov 1;3(11):1529-1537. PubMed PMID: 28687838
  2. Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li RUnsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical Cancer Research. 2017 Jul 1;23(13):3334-3342 PMID: 28073839
  3. Wu J, Li B, Sun X, Cao G, Rubin DL, Napel S, Ikeda DM, Kurian AW, Li RHeterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer. Radiology. 2017 Nov;285(2):401-413. PubMed PMID: 28708462
  4. Ren S, Hara W, Wang L, Buyyounouski MK, Le QT, Xing L, Li RRobust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. Int J Radiat Oncol Biol Phys. 2017 Mar 15;97(4):849-857. PubMed PMID: 28244422


  1. Cui Y, Tha KK, Terasaka S, Yamaguchi S, Wang J, Kudo K, Xing L, Shirato H, Li RPrognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images. Radiology. 2016 Feb;278(2):546-53 PubMed PMID: 26348233
  2. Wu J, Aguilera T, Shultz D, Gudur M, Rubin DL, Loo BW Jr, Diehn M, Li REarly-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of 18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. Radiology. 2016. 281(1):270-8. PubMed PMID: 27046074
  3. Cui Y, Song J, Pollom E, Alagappan M, Shirato H, Chang DT, Koong AC, Li RQuantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys. 2016 Sep 1;96(1):102-9. PubMed PMID: 27511850


  1. Gudur MS, Hara W, Le QT, Wang L, Xing L, Li RA unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Phys Med Biol. 2014 Nov 7;59(21):6595-606. PubMed PMID: 25321341

    A complete list of publications is available at: