Digital pathology and AI: a risk stratification method for follicular lymphoma

Author(s): Dr Martin Fergie, Dr Anna-Maria Tsakiroglou, Dr Richard Byers, Dr Kim Linton

Hospital: University of Manchester, Spotlight Pathology, Manchester University Hospital, The Christie NHS Foundation Trust

Reference: RAD Magazine, 48, 567, 22-23


The evolving maturity and adoption of digital pathology platforms has created an opportunity for AI to improve the objectivity of tissue analysis, opening new avenues for biomarker research and improved cancer diagnostics. Key examples in routine clinical use include systems for automated detection of cancerous regions in prostate core biopsies and metastases in lymph node biopsies. As well as reproducing routine assessments, AI can predict molecular status directly from H&E sections, with potential for high throughput biomarker screening at a reduced cost.

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