Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
NTUST Center of Computer Vision and Medical Imaging, Taipei, Taiwan.
Bioinformatics. 2018 May 15;34(10):1767-1773. doi: 10.1093/bioinformatics/btx838.
The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients' background information, histopathological and immunohistochemical tissue images.
A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017- A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem.
The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/).
Supplementary data are available at Bioinformatics online.
精准医学的目标是利用新知识和技术来优化干预措施的时机和靶向性，以获得最大的治疗效果。本研究探讨了在甲状腺癌诊断中，在没有精确像素级注释的情况下构建人工智能模型来预测肿瘤大小、甲状腺外延伸、淋巴结转移、癌症分期和 BRAF 突变的可能性，提供了患者的背景信息、组织病理学和免疫组织化学图像。
在 2017 年 IEEE 国际生物医学成像研讨会的支持下，建立了一个客观评估自动患者诊断算法的新框架——甲状腺癌诊断中的组织微阵列分析大挑战。在这里，我们展示了挑战的数据、方法和结果，并为未来使用这个基准奠定了原则。该挑战的主要贡献包括：创建组织微阵列数据存储库；创建甲状腺癌临床诊断分类数据存储库；定义客观的定量评估，用于比较和排名算法。使用这个基准，比较了三种自动预测五种临床结果的方法，并在本文中给出了详细的定量评估结果。基于定量评估结果，我们认为自动患者诊断仍然是一个具有挑战性且尚未解决的问题。
数据集和评估软件将提供给研究界，进一步鼓励该领域的未来发展。（http://www-o.ntust.edu.tw/cvmi/ISBI2017/）。
补充数据可在《生物信息学》在线获取。
