Artificial Intelligence Biomarker Development on Breast Cancer Histology
Our deep learning model for risk of recurrence in HR+/HER2- breast cancer identifies an area of lymphovascular invasion indicating high risk disease.
Welcome to the Howard Lab Website
Our lab’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?
News
A Clinical Tool for Accurate Prediction of Breast Cancer Recurrence
Given high costs of Oncotype DX testing, widely used in recurrence risk assessment for early-stage breast cancer, studies have predicted Oncotype using quantitative clinicopathologic variables. However, such models have incorporated only small cohorts. Using a cohort...
The Increase in Artificial Intelligence Content in Oncology Abstracts
Artificial intelligence (AI) models can generate scientific abstracts that are difficult to distinguish from the work of human authors. The use of AI in scientific writing and performance of AI detection tools are poorly characterized. We conducted a study to help...
Clarifying the Prognostic Implications of HER2-Low Breast Cancer
Given conflicting results regarding the prognosis of erb-b2 receptor tyrosine kinase 2 (ERBB2; formerly HER2 or HER2/neu)–low breast cancer, a large-scale, nationally applicable comparison of ERBB2-low vs ERBB2-negative breast cancer is needed. We conducted a...