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April 5, 2026Cancer Research

Image-Based ROI Selection for Spatial Transcriptomics in Gastric Cancer with ICI Outcomes

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Authors

SPSunho ParkVanderbilt University Medical CenterMKMinji KimVanderbilt University Medical CenterJCJean R. ClemenceauVanderbilt University Medical Center

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Implication

Image-based approach predicts outcomes in gastric cancer, highlighting regions for spatial transcriptomic profiling.

Key Points

  • This research focuses on developing a method for selecting regions of interest (ROIs) in gastric cancer based on predicted immune checkpoint inhibitor (ICI) treatment outcomes.
  • Assembled 157 whole slide images from gastric cancer patients treated with ICIs.
  • Utilized AI classifiers to identify tumor tiles and predict treatment responses.
  • Generated heatmaps for predicted ICI responsiveness and derived ROIs using a sliding window approach.
  • Achieved slide-level AUC exceeding 0.7 for predicting responder vs. non-responder status.
  • Identified candidate ROIs maximizing predicted responsiveness, non-responsiveness, or mixed patterns.
  • Developed a multiprocessing pipeline for efficient ROI suggestion generation within seconds.

Cite This Study

Park et al. (2026) studied this question.

synapsesocial.com/papers/69d1fe18a79560c99a0a4a84https://doi.org/10.1158/1538-7445.am2026-1420
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Abstract 3957: Spatially derived transcriptomic predictors of immune checkpoint blockade outcome in advance gastric cancer2026
  2. 2Abstract 7400: AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer2024
  3. 3Abstract 4170: Artificial intelligence (AI)-based multi-modal approach using H&E and CT image for predicting treatment response of immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC)2024 · 4 citations
  4. 4Abstract 3932: Spatial immune checkpoint profiling reveals predictive biomarkers of immunotherapy response in oral squamous cell carcinoma.2026
  5. 5Abstract A065: Fast and cost-effective prediction of treatment response in head and neck cancer by characterizing the tumor microenvironment from routine H&E slides2026