Howard Lab

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

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...

Clarifying the Prognostic Implications of HER2-Low Breast Cancer

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...

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