In:
Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-06-10)
Abstract:
The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P 〈 .001) and isolated tumor cells (67.95% to 96.15%, P 〈 .001) in a significantly shorter review time (−31.5%, P 〈 .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).
Type of Medium:
Online Resource
ISSN:
2041-1723
DOI:
10.1038/s41467-022-30746-1
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
2553671-0
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