利用肝脏 CT 扫描进行结直肠癌基因组突变分类
Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification
AI总结：
本文提出基于深度学习的肝脏 CT 影像分析流程，以无创方式识别结直肠癌 RAS 突变家族，并在小样本数据集上达到 0.73 F1 分数。
摘要：
肝脏是结直肠癌（colon-rectal cancer，CRC）患者发生远处转移时最常受累的器官，因此有必要了解病灶的突变状态，以便正确制定最佳的个体化治疗方案。迄今为止，研究人员已致力于开发无创、实时的方法，以实现对整个肿瘤的分析，并利用新的人工智能工具分析通过计算机断层扫描（CT）获得的肿瘤图像。为应对当前以活检分析为基础的医疗工作流程，据我们所知，我们提出了首个基于深度学习的探索，从患者医学影像出发进行此类分类。我们提出：i）一条用于处理可用 CT 扫描样本量不足数据集的稳健流程；ii）一项用于基因组突变诊断支持的基线研究，以服务于预防性患者随访。我们的方法能够从 CT 图像中识别 CRC RAS 突变家族，F1 分数为 0.73。
英文摘要：
The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
