Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration
Yesul Jeong, Sangjeong Ahn, Sung Hak Lee · 2026
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Description
Yesul Jeong, Sangjeong Ahn, and Sung Hak Lee review AI tools that read stomach cancer slides to predict biomarkers, in a 2026 journal article. Gastric cancer varies a lot at the molecular level, and the standard gene and protein tests are costly, slow, and hungry for tissue.
The review sums up work on AI that reads whole-slide images of standard stained tissue to infer molecular subtype, gene changes open to drugs, and immune traits of the tumor setting. Models have shown promise at spotting traits such as microsatellite instability and Epstein-Barr virus status, as the authors report. Blending image data with other data types is the next step they trace. Such tools could sort patients for precise care faster and with less tissue than lab tests alone.
Catalog record
| CREATOR | Yesul Jeong, Sangjeong Ahn, Sung Hak Lee |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 64337 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |