by rcd2365 | Jun 22, 2024 | Publications
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...
by rcd2365 | Jun 22, 2024 | Publications
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...
by rcd2365 | Oct 12, 2023 | Publications
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...
by rcd2365 | Oct 12, 2023 | Publications
Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource...
by rcd2365 | Oct 12, 2023 | Publications
Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of...
by rcd2365 | Oct 12, 2023 | Publications
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations....
