Research Areas

Imaging biomarkers

We have been developing image-based biomarkers to improve prediction of treatment response and prognosis for personalized cancer therapy. Our approach incorporates deep learning as well as quantitative radiomic profiling of the intratumoral subregions/habitats and surrounding parenchyma. We have conducted imaging biomarker studies on breast, lung, head and neck, and gastric cancers, using radiologic modalities such as PET, CT, and MRI (Cui et al. Radiology, 2016; Wu et al. Radiology, 2016, 2017, 2018; Wu et al. J Nucl Med, 2019, Jiang et al. Annals Surgery, 2020). 

We are also exploring the biological basis of clinically relevant radiologic phenotypes by correlating imaging with molecular data (Wu et al. Clin Cancer Res, 2017). We recently developed a radiomic-immune signature that predicts response and survival outcomes after chemotherapy (Jiang et al. Annals Oncology, 2020).

In ongoing work, we are investigating artificial intelligence approaches, specifically deep learning, to analyze and extract information from histopathologic images for predicting cancer genomics and treatment outcomes (Wang et al. Cancers, 2020).

Molecular biomarkers

In addition to imaging, we are also developing molecular biomarkers to predict therapy response and prognosis in cancer. We are particularly interested in how various components of the tumor microenvironment (including immune and stroma) interact with each other and contribute to cancer progression or control. 

Previously we developed an individualized immune gene expression signature that predicts survival of patients with non-small cell lung cancer (Li et al. JAMA Oncology, 2017).  In another work, we integrated radiation sensitivity and immune signatures to predict which patients are most likely to derive survival benefit from adjuvant radiotherapy in breast cancer (Cui et al. Clin Cancer Res, 2018). In a study of pan-squamous cell carcinomas (including esophagus, head and neck, lung, and cervix), we identified 6 immune subtypes associated with distinct molecular characteristics and clinical outcomes (Li et al. Clin Cancer Res, 2019).  

In ongoing work, we are investigating machine learning approaches for analyzing genetic and epigenetic profiles and developing molecular biomarkers for early cancer detection, prognostication, and response prediction.


Our lab is currently being supported by several NIH grants from the National Cancer Institute (R01 CA193730, R01 CA222512, R01 CA233578). My previous research was supported by an NIH Pathway to Independence Award (K99/R00 CA166186) from 2012 to 2017.