Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Michel E. Vandenberghe; Marietta Scott; Paul W. Scorer; Magnus Söderberg; Denis Balcerzak; Craig Barker · 2017 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour r
Abstract by Michel E. Vandenberghe; Marietta Scott; Paul W. Scorer; Magnus Söderberg; Denis Balcerzak; Craig Barker, Scientific Reports (2017) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/srep45938