Hua Li researching head and neck and cervical cancers with more than $2.3 million in NIH support


Susan McKenna

Hua Li, adjunct research associate professor in Bioengineering and the Carle Illinois College of Medicine at the University of Illinois at Urbana-Champaign and medical physicist in the Carle Cancer Center, Urbana, Ill., received more than $2.3 million in research funds from the National Institutes of Health (NIH) National Cancer Institute (NCI) to support two cancer-related projects. 

Li's awards include more than $2 million for a five-year R01 project, “Multimodal Biomarkers for Oropharyngeal Cancer.”

Oropharyngeal (head and neck) cancers are the fifth most common cancer type in the United States, with an overall survival rate lower than 50 percent. Although the incidence of other subsites of head and neck cancer has decreased steadily in past decades, the number of oropharyngeal squamous cell carcinoma (OPSCC) cases has increased significantly. Most OPSCC patients receive standard cancer therapy. However, the clinical outcomes vary significantly and are difficult to predict.

Predicting early whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing individualized care for cancer patients. Human papillomavirus (HPV) is a known driving factor in oropharyngeal cancer, as well as a significant prognostic biomarker for patient survival. However, regarding metastatic spread, HPV-positive oropharyngeal cancer patients have similar rates to HPV-negative patients. The same is true for patient groups stratified with other clinical biomarkers.

With previous demonstration of the prognostic value and the associated challenges and limitations of clinical biomarkers, Li and her collaborators at Washington University School of Medicine in St. Louis see the need for the investigation of more robust biomarkers. They are using artificial intelligence approaches to accurately stratify patients for individualized and more effective treatment.

The major goal of the research is to explore informative imaging and genomic biomarkers and use them to develop a multimodal biomarker-based clinical decision-making tool that can reliably predict subsets of patients with low and high risks for treatment failure. The team is proposing a novel machine-learning-based strategy to effectively identify and seamlessly combine prognostic information carried by multimodal biomarkers.

Biomarkers extracted from medical images are a promising and exciting new class of cancer biomarkers for characterizing tumor habitats, which have shown promise in accurately separating favorable and unfavorable prognosis patients for several tumor sites.

In other, related research, Li received $370,000 in research funding through an NCI R21 award to support her project, “Radiomics-based Prognostic Model of Cervical Cancer Habitats." With this award, Li and her collaborators at Washington University School of Medicine in St. Louis are exploring prognosis information carried by multi-modality images acquired before and during treatment for cervical cancer. They are combining this information with advanced machine-learning techniques to develop first-of-its-kind prognostic modeling that can provide supportive information to assist clinicians in determining more effective radiation treatment strategies for cervical cancer patients.

These NIH-supported projects are expected to provide a clinically useful family of models for predicting disease control outcomes from current cancer treatments. The resulting tool is intended to provide useful information to support patient stratification and individualized treatment. The researchers anticipate that many of the methodological improvements established in these projects will not only improve cancer treatment in other disease sites but also may have impacts on disease diagnosis when multi-modal information is available.