Publications by authors named "Kajsa Ledesma Eriksson"
Article Synopsis
- Stratipath Breast is an AI tool designed to classify breast cancer patients into high- and low-risk categories based on histopathology images, specifically using H&E-stained slides.
- A study involving 2,719 patients from two Swedish hospitals evaluated this tool's prognostic performance, focusing on progression-free survival (PFS) among patients, particularly those with ER-positive/HER2-negative tumors.
- Results showed that Stratipath Breast effectively predicts risk in various breast cancer subgroups, suggesting its potential to guide treatment decisions and reduce unnecessary therapies at a lower cost compared to molecular methods.
Article Synopsis
- The alignment of tissue in whole-slide images (WSI) is essential for both research and clinical purposes, and recent advancements in computing and deep learning have changed how these images are analyzed.
- The ACROBAT challenge was organized to evaluate various WSI registration algorithms using a large dataset of 4,212 WSIs from breast cancer patients, aiming to align tissue stained with different methods.
- The study found that various WSI registration methods can achieve high accuracy and identified specific clinical factors that affect their performance, helping researchers choose and improve their analysis techniques.
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets.
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