The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

Yan Chen, Zepang Sun, Wanlan Chen, Changyan Liu, Ruoyang Chai, Jingjing Ding, Wen Liu, Xianzhen Feng et al.

Frontiers in Immunology 2021 article cited by 71

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Cancer Immunotherapy and Biomarkers oncology stage: research

Problem

Gastric cancer responds heterogeneously to immunotherapy, and identifying immune subtypes could improve therapeutic strategies.

Why it matters

Better immune stratification could improve immunotherapy outcomes for gastric cancer patients.

What the AI did

Consensus clustering identified immune subtypes from genomic data, and a deep learning model on pathological whole-slide images was constructed to identify those subtypes.

Results

Three reproducible immune subtypes identified and validated in independent cohorts; DL model performance not stated in source

Impact

Validated immune subtypes may inform immunotherapeutic strategies for gastric cancer.

Record

Venue	Frontiers in Immunology
Published	2021
Work type	article
Authors	Yan Chen, Zepang Sun, Wanlan Chen, Changyan Liu, Ruoyang Chai, Jingjing Ding, Wen Liu, Xianzhen Feng et al.
Citations	71 (OpenAlex, at import time)
References	53
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3389/fimmu.2021.685992
Open access	gold
Keywords	gastric cancer, immune subtypes, deep learning, whole-slide images

Importance rating

7 / 10

significant — thousands to millions affected

Gastric cancer immunotherapy stratification could improve outcomes for a major heterogeneous cancer; abstract gives no scale or burden figures.

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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