1. We received an NIH R01 grant from the NIDCR to develop radiomics and deep learning-based prognostic imaging biomarkers for risk stratification and personalized therapy of oropharengeal cancer. Dr. Quynh Le is multi-PI of the grant. We will work with the NRG Oncology to prospectively validate these imaging biomarkers. [Jul 2022]


  1. Our work on a unifying radiological tumor classification was published in Nature Machine Intelligence. The full-text paper can be read here for free. See also News and Views published in the same journal and report by Stanford Institute for Human-Centered AI. [Sep 2021]
  2. Congratulations to Cheng Jin on accepting a tenure-track faculty position in the School of Biomedical Engineering at the Shanghai Jiaotaong University in China. [Sep 2021]


  1. Our paper on a deep learning CT signature to predict prognosis and chemotherapy benefit in gastric cancer was published in Annals of Surgery. This work was also reported by Reuters Health. [Jan 2020]
  2. Congratulations to Jia Wu on getting a tenure-track faculty position from the MD Anderson Cancer Center!  He will join the Departments of Imaging Physics and Thoracic & Head Neck Medical Oncology. [Jan 2020]


  1. The book Radiomics and Radiogenomics: Technical Basis and Clinical Applications edited by Ruijiang Li et al. has been published by the CRC Press. It provides a first complete overview and summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. [Jul 2019]
  2. Our work on the immune landscape of squamous cell carcinoma (SCC) was published in Clinical Cancer Research. In this study, we identified 6 immune subtypes of SCC associated with distinct molecular characteristics and clinical outcomes. Our findings may be relevant for designing combination treatment strategies and guiding optimal selection of patients for immunotherapy. [Mar 2019]
  3. We received a new R01 grant from the NCI to develop imaging biomarkers in lung cancer. In collaboration with Diehn and Loo labs, we will integrate imaging with circulating tumor DNA to improve prognostication. [Feb 2019]


  1. Our work on a radiotherapy predictive biomarker was published in Clinical Cancer Research. In this work, we developed integrated radiosensitivity and immune gene expression signatures to identify patients who are most likely to benefit from radiotherapy in breast cancer. This article is featured in Highlights of This Issue. [Aug 2018]
  2. Our work on a prognostic imaging marker for breast cancer was published in Radiology. We proposed a novel metric for measuring intratumoral spatial heterogeneity that predicts recurrence-free survival after neoadjuvant chemotherapy for breast cancer. This work was discussed in an accompanying editorial by Robert Gillies [May 2018].
  3. Congratulations to Dr. Jia Wu for receiving the NIH Pathway to Independence Award! This highly competitive K99/R00 grant administered by the NCI provides 5-year funding that will help him transition to an independent acadamic career. [Feb 2018]
  4. We received an R01 grant from the NCI to develop prognostic and predictive imaging biomarkers in breast cancer as well as to identify their molecular basis. [Feb 2018]
  5. Our work on a prognostic imaging signature for early stage lung cancer was published in a special issue 'Imaging in Radiation Oncology' of the Red Journal. In this study, we elucidated the molecular basis of a CT imaging signature that predicted survival in multiple patient cohorts.  When combined with established prognosticators, the imaging signature improves survival prediciton. [Jan 2018]


  1. Our work on an immune signature in lung cancer was published in JAMA Oncology. In this study, an immune signature of 25 gene pairs was developed and validated to be highly prognostic of survival in early stage non-squamous non-small cell lung cancer. [Jul 2017]
  2. Our work on breast cancer imaging subtypes was published in Clinical Cancer Research. This study discovered novel breast cancer subtypes based on tumor and parenchymal imaging phenotypes, and elucidated their molecular underpinnings and prognostic signaficance. [Jan 2017]


  1. Our first R01 grant was funded by the NCI on December 18, 2015. The goal of this project is to develop image analysis and computational tools to enalbe MRI-based radiation treatment planning. [Dec 2015]
  2. Congratulations to Dr. Yi Cui for winning the ASTRO Basic/Translational Science Abstract Award! He will present his work on novel prognostic imaging biomarkers in pancreatic cancer at 2015 ASTRO annual meeting. This work will also be presented at 2015 AAPM Science Council Research Symposium. [Sep 2015]


  1. Congratulations to Dr. Madhu Gudur for winning the ASTRO Resident Clinical/Basic Science Research award! His work entitled ‘A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning’ was presented at 2014 ASTRO annual meeting. [Sep 2014]
  2. Our NIH/NCI R00 grant was funded on September 1, 2014. The goal of this project is to develop on-board real-time volumetric imaging methods for lung cancer radiotherapy. [Sep 2014]