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English(EN) Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction

新的图神经网络可预测乳腺癌治疗反应

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的3D时空框架，使用图神经网络来预测乳腺癌患者的治疗反应。该方法模拟医学影像数据中的时间交互，并结合自监督学习目标进行轨迹表示。在ISPY-2数据集上对585名患者进行的实验显示，与现有的视觉和自监督学习基线相比，该方法表现出显著的优越性，为预测病理完全缓解（pCR）设定了新基准。研究还分析了可用影像时间点和扫描间时间差异的影响，强调了纵向医学影像在治疗预测中的价值。 AI

影响 为预测癌症治疗反应设定了新基准，有可能通过个性化医疗改善患者预后。

排序理由 详细介绍医学影像分析和预测新方法的学术论文。

在 arXiv cs.AI 阅读 →

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新的图神经网络可预测乳腺癌治疗反应

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详细介绍医学影像分析和预测新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Johannes Kiechle, Richard Osuala, Daniel M. Lang, Stefan M. Fischer, Ivana Jan\'i\v{c}kov\'a, Karim Lekadir, Julia A. Schnabel, Jan C. Peeken ·

    用于治疗反应预测的纵向医学影像轨迹的图表示学习

    arXiv:2607.04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective t…

  2. arXiv cs.AI TIER_1 English(EN) · Jan C. Peeken ·

    用于治疗反应预测的纵向医学影像轨迹的图表示学习

    In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as t…

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