Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Wei Ba, Shuhao Wang, Meixia Shang, Ziyan Zhang, Huan Gan Wu, Chunkai Yu, Ranran Xing, Wenjuan Wang et al.

Modern Pathology 2022 article cited by 76

Source Open-access PDF More in Medicine & Health

importance 6/10 ai assisted AI in cancer detection cancer pathology stage: research

Problem

Improving pathologists' accuracy in diagnosing gastric cancer from whole-slide images.

Why it matters

Accurate pathological diagnosis of gastric cancer directly affects patient treatment decisions.

What the AI did

A deep learning system assisted 16 board-certified pathologists interpreting 110 whole-slide images in a fully crossed multireader multicase study with washout.

Results

DL-assisted ROC-AUC 0.911 vs 0.863 unassisted (P=0.003); sensitivity 90.63% vs 82.75% (P=0.010); specificity not significantly different.

Impact

Peer-reviewed reader study shows DL assistance measurably improves pathologist diagnostic performance.

Record

Venue	Modern Pathology
Published	2022
Work type	article
Authors	Wei Ba, Shuhao Wang, Meixia Shang, Ziyan Zhang, Huan Gan Wu, Chunkai Yu, Ranran Xing, Wenjuan Wang et al.
Citations	76 (OpenAlex, at import time)
References	31
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41379-022-01073-z
Open access	hybrid
Keywords	deep learning, pathology, gastric cancer, whole-slide images, diagnostic assistance, ROC-AUC

Importance rating

6 / 10

significant — thousands to millions affected

Accurate gastric cancer pathology guides treatment, but abstract reports only a 110-slide, 16-pathologist reader study; disease burden or population scale not 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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