Mentor Areas
Professor Rong Zhou will provide guidance on MRI data acquisition methods and generation of parameter maps with specific physiological meaning such as cell density and blood perfusion. She will also mentor the students about using animal models to study pancreatic cancer responses to therapies as her lab has done extensive study in testing chemotherapy, stroma-targeted therapy and novel KRAS inhibitor therapy in mouse models of pancreatic cancer.
Professor Yong Fan of Radiology, a colleague and collaborator of Dr. Zhou, is an expert in machine learning and will mentor the students in the area of MRI based radiomics, extracting of image habitats, and applying AI-based methods to habitats or radiomics for prediction when cancer will become resistant to KRAS inhibitor therapy.
Description:
Over 95% of pancreatic ductal adenocarcinoma (PDAC) carries mutation in an oncogene called KRAS, which is a key driver of PDAC initiation and progression. Small molecule KRAS inhibitors (KRASi) represent a promising new treatment for this deadly cancer. In PDAC models, we examined a clinically ready imaging protocol that combines MRI-based tumor size (from anatomical MRI), diffusion-weighted MRI (DWI), dynamic contrast enhanced MRI (DCE) for detection of early response as well as acquired resistance to a novel KRASi. This work has recently been published in Clinical Cancer Research (https://pubmed.ncbi.nlm.nih.gov/40260640/).
Based on these results, we are now developing methods to extract radiomics and image habitats from physiological (DWI and DCE) and anatomical MRI mentioned above to integrate with ML for predicting when the cancer will become resistant to KRASi therapy. Capability of early predictions can be applied to patients thus enable physicians to select combinational treatment to overcome resistance and prolong the patient’s life.
Preferred Qualifications
This project is perfect for students who are interested in medical imaging and integrating ML/AI in image processing to study cancer responses and resistance to therapies. A background in quantitative science including Math (Ordinary differential equations and statistics), programming (e.g., python) and basics in biology and physiology will be very helpful to carry on the project.
We encourage both independence and frequent communication with the mentors (detailed in section below). Importantly, we acknowledge a significant contribution made by the students by authorship of the manuscript of the subject research.
Project Website
Learn more about the researcher and/or the project here. This site shows the investigator team and our imaging research in pancreatic ca…
Details:
Preferred Student Year
First-year, Second-Year, Junior, Senior
Academic Term
Fall, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.