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

AI利用多模态学习预测GIST伊马替尼反应 · 跟踪2个来源

研究人员开发了一个使用交叉注意力的多模态深度学习框架,用于预测胃肠道间质瘤(GISTs)患者对新辅助伊马替尼治疗的反应。该模型整合了计算机断层扫描(CT)影像和临床变量,取得了较高的内部性能(AUC高达0.99),但外部性能较为适中(AUC 0.60-0.63)。可解释性分析揭示了响应者和非响应者之间特征重要性存在显著差异,为治疗反应决定因素提供了见解。 AI

影响 这项研究展示了AI在通过更准确地预测治疗反应来改善GIST患者个性化医疗方面的潜力。

排序理由 该集群包含一篇详细介绍新研究方法和发现的学术论文。

在 arXiv cs.CV 阅读 →

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AI利用多模态学习预测GIST伊马替尼反应 · 跟踪2个来源

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报道来源 [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… ·

    胃肠道间质瘤新辅助伊马替尼治疗反应预测的交叉注意力多模态学习:一项多中心回顾性研究

    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 ·

    胃肠道间质瘤新辅助伊马替尼治疗反应预测的交叉注意力多模态学习:一项多中心回顾性研究

    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…