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AI predicts imatinib response in GIST using multimodal learning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a multimodal deep learning framework using cross-attention to predict patient response to neoadjuvant imatinib for gastrointestinal stromal tumors (GISTs). The model integrates computed tomography (CT) imaging with clinical variables, achieving high internal performance (AUC up to 0.99) but more modest external performance (AUC 0.60-0.63). Explainability analyses revealed significant differences in feature importance between responders and non-responders, offering insights into treatment response determinants. AI

IMPACT This research demonstrates the potential of AI in improving personalized medicine for GIST patients by predicting treatment response more accurately.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and findings.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI predicts imatinib response in GIST using multimodal learning · 2 sources tracked

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The cluster contains an academic paper detailing a new research methodology and findings.
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76 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Fariba Tohidinezhad, Douwe J. Spaanderman, Natalia Oviedo Acosta, Kaouther Mouheb, Karthik Prathaban, David F. Hanff, Dirk J. Gr\"unhagen, Cornelis Verhoef, Joris M. van Sabben, Evelyne Roets, Jette J. Slettenhaar, Hans Gelderblom, Ingrid M. E. Desar, An… ·

    Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

    arXiv:2606.25579v1 Announce Type: cross Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical or molecular markers. This study developed and evaluated an explain…

  2. arXiv cs.CV TIER_1 English(EN) · Martijn P. A. Starmans ·

    Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

    Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical or molecular markers. This study developed and evaluated an explainable multimodal deep learning framework integratin…

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