Titles and Affiliations

Hematology/Oncology Fellow
Conquer Cancer, The ASCO Foundation
The University of Chicago
Chicago, Illinois

Research area

Identifying a new digital pathology biomarker for recurrence in early breast cancer.


OncotypeDx® (ODX) is a genomic test that helps predict the risk of recurrence and chemotherapy benefit for early-stage hormone receptor (HR)–positive breast cancer. However, ODX testing is expensive, can take several weeks to perform, and is not available in low resource settings worldwide. Moreover, studies have shown that ODX may be less accurate and used less frequently in black patients, contributing to healthcare disparities. While not all breast cancer patients receive ODX testing, all do undergo a biopsy that is examined by pathologists. Increasingly, artificial intelligence (AI) technology is being used to augment pathological review of biopsies by identifying features unseen by the human eye—including mutations as well as patterns of activation of important genes. With his Conquer Cancer Young Investigator Award, supported by BCRF, Dr. Howard will explore if AI applied to biopsies can serve as an alternative to ODX testing.

What’s next

Early data suggests that AI can infer ODX directly from digital images of breast cancer, and therefore predict the risk of distant recurrence and the benefit of chemotherapy. However, this approach must be tested in a racially and ethnically diverse dataset to ensure it can be equitably applied to all patients. To this end, Dr. Howard will validate an AI predictor for ODX in the Chicago Multi-ethnic Cohort of Breast Cancer. This approach could allow for rapid recognition of patients in need of chemotherapy to reduce treatment delays, reduce the cost of care, and make the data that ODX provides available equitably and in low resource settings worldwide. 


Frederick M. Howard, MD, is a Hematology/Oncology Fellow at the University of Chicago Department of Medicine in Chicago, Illinois. He received his medical degree from the University of Michigan and completed his residency and chief residency in Internal Medicine at Yale-New Haven Hospital in New Haven, Connecticut. 

Dr. Howard's research focuses on answering several important questions at the intersection of digital health and breast medical oncology: 1) Can artificial intelligence be used to improve prediction of response to therapy in breast cancer, and thus lead to better personalization of therapies? 2) Can deep learning use readily available pathologic and imaging data to improve upon or supplement existing genomic biomarkers in breast cancer in order to reduce cost, prevent unnecessary treatment delays, and improve access to biomarkers? and 3) Given the rapid growth of big data/artificial intelligence tools in oncology, what safeguards need to be in place to ensure these tools do not recapitulate healthcare disparities that are currently prevalent in cancer care?

BCRF Investigator Since


Areas of Focus