AI reads scans and slides to outsmart stomach cancer

NCT ID NCT07454967

First seen Jun 25, 2026 · Last updated Jul 30, 2026 · Updated 3 times

Summary

This study tested an artificial intelligence model that combines CT scans, digital pathology images, and patient history to improve diagnosis and risk prediction for gastrointestinal stromal tumors (GISTs). Researchers enrolled 300 patients with suspected stomach tumors. The AI aims to tell GISTs apart from other tumors, predict how aggressive they are, and guess genetic mutations—all without needing surgery. The results were compared to standard lab tests, but the AI's predictions were not shared with doctors, so it did not affect patient care.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance
Multimodal AI Analysis System (GIST-RadPath-AI Model)
What this could lead to
If successful, this AI could help doctors diagnose GISTs more accurately without invasive surgery and predict tumor risk and genetic mutations to guide treatment.
What could go wrong
This is an early observational study with 300 participants, so the AI may not work as well in larger or more diverse groups. The results are blinded and not used in patient care, so real-world benefits are unproven.

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Conditions

The condition(s) this trial relates to.

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Contacts and locations

Locations

  • Baoding Central Hospital

    Baoding, Hebei, 071030, China

  • Cangzhou People's Hospital

    Cangzhou, Hebei, 061000, China

  • Hengshui People's Hospital

    Hengshui, Hebei, 053099, China

  • Jinling Hospital

    Nanjing, Jiangsu, 210002, China

  • Renmin Hospital of Wuhan University

    Wuhan, Hubei, 430065, China

  • Shijiazhuang People's Hospital

    Shijiazhuang, Hebei, 050011, China

  • The Fifth Affiliated Hospital of Anhui Medical University

    Fuyang, Anhui, 236003, China

  • The First Affiliated Hospital of University of South China

    Hengyang, Hunan, 421001, China

  • The Second Affiliated Hospital of Xingtai Medical College

    Xingtai, Hebei, 054000, China

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Other studies related to the condition(s) this trial covers.