Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy
Maxime W. Lafarge; Enric Domingo; Korsuk Sirinukunwattana; Ruby Wood; Leslie Samuel; Graeme Murray; Susan D. Richman; Andrew Blake · 2024 · npj Precision Oncology
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
Abstract The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin
Abstract by Maxime W. Lafarge; Enric Domingo; Korsuk Sirinukunwattana; Ruby Wood; Leslie Samuel; Graeme Murray; Susan D. Richman; Andrew Blake, npj Precision Oncology (2024) — licensed CC BY 4.0.
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Metadata source: DOAJ · DOI 10.1038/s41698-024-00580-3