Liu Lu, Parker Kevin J, Jung Sin-Ho
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA.
J Pers Med. 2021 Nov 4;11(11):1150. doi: 10.3390/jpm11111150.
Imaging is important in cancer diagnostics. It takes a long period of medical training and clinical experience for radiologists to be able to accurately interpret diagnostic images. With the advance of big data analysis, machine learning and AI-based devices are currently under development and taking a role in imaging diagnostics. If an AI-based imaging device can read the image as accurately as experienced radiologists, it may be able to help radiologists increase the accuracy of their reading and manage their workloads. In this paper, we consider two potential study objectives of a clinical trial to evaluate an AI-based device for breast cancer diagnosis by comparing its concordance with human radiologists. We propose statistical design and analysis methods for each study objective. Extensive numerical studies are conducted to show that the proposed statistical testing methods control the type I error rate accurately and the design methods provide required sample sizes with statistical powers close to pre-specified nominal levels. The proposed methods were successfully used to design and analyze a real device trial.
成像在癌症诊断中很重要。放射科医生需要经过长时间的医学培训和临床经验积累，才能准确解读诊断图像。随着大数据分析的发展，基于机器学习和人工智能的设备目前正在研发中，并在成像诊断中发挥作用。如果基于人工智能的成像设备能够像经验丰富的放射科医生一样准确地读取图像，那么它或许能够帮助放射科医生提高读取的准确性并管理其工作量。在本文中，我们考虑了一项临床试验的两个潜在研究目标，即通过比较基于人工智能的设备与人类放射科医生的一致性来评估该设备用于乳腺癌诊断的效果。我们针对每个研究目标提出了统计设计和分析方法。进行了大量的数值研究，结果表明所提出的统计检验方法能够准确控制第一类错误率，并且设计方法能够提供所需的样本量，其统计功效接近预先指定的名义水平。所提出的方法已成功用于设计和分析一项实际的设备试验。
