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English(EN) Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies

AI模型利用转录组和组织病理学预测乳腺癌治疗反应

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的两阶段AI模型，旨在预测乳腺癌患者新辅助治疗的病理完全缓解（pCR）。该模型首先从大量数据集的组织病理图像中推断转录组，然后利用推断的表达与临床变量一起预测治疗反应。该方法实现了0.79的汇总AUROC，并证明其性能优于传统的组织病理学生物标志物，同时即使在组织样本量很少的情况下也表现出鲁棒性。 AI

影响 该AI模型可以通过从活检数据中更准确地预测治疗反应来改善精准肿瘤学。

排序理由 该集群包含一篇详细介绍新AI模型及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI模型利用转录组和组织病理学预测乳腺癌治疗反应

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该集群包含一篇详细介绍新AI模型及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jungkyu Park, Dhruva Biswas, Joseph Cappadona, Cerise Tang, Ken G. Zeng, Bartosz Machura, Chuwen Liu, Paolo Tarantino, Coral Omene, Francisco J. Esteva, Rohit Bhargava, Marcin Braun, Kamila Pa\'zdzierz, Jakub Czerwi\'nski, Hanna Roma\'nska-Knight, Albert… ·

    基于转录组信息的、多模态人工智能用于预测乳腺癌活检新辅助治疗反应

    arXiv:2610.03693v1 Announce Type: new Abstract: Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant therapy in breast cancer. The first stage learns the…

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