Deep learning for survival analysis in breast cancer with whole slide image data

Huidong Liu, Tahsin Kurç

Bioinformatics 2022 article cited by 29

Source Open-access PDF More in Medicine & Health

importance 6/10 partially solved AI in cancer detection oncology stage: research open-source model

Problem

Predicting breast cancer survival from whole slide images is difficult due to data size, complexity, and limited training data.

Why it matters

Better prognostic biomarkers could improve cancer diagnosis and understanding of disease mechanisms.

What the AI did

A multi-resolution deep learning method integrated image data at multiple resolutions with deep-learning tumor, lymphocyte, and nuclear segmentation results for survival analysis.

Results

c-index of 0.706 vs 0.551 for an approach using only color image data

Impact

The method significantly improved survival prediction over an image-only baseline in experimental evaluation.

Record

Venue	Bioinformatics
Published	2022
Work type	article
Authors	Huidong Liu, Tahsin Kurç
Citations	29 (OpenAlex, at import time)
References	33
Model or method	multi-resolution deep learning method
Organization	not stated in source
Primary source	https://doi.org/10.1093/bioinformatics/btac381
Open access	green
Keywords	survival analysis, whole slide images, deep learning, breast cancer, c-index

Importance rating

6 / 10

significant — thousands to millions affected

Better prognostic biomarkers from whole-slide images could improve breast cancer diagnosis, prognosis, and understanding of disease mechanisms; no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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