Yang Yi, Jin Gang, Pang Yao, Wang Wenhao, Zhang Hongyi, Tuo Guangxin, Wu Peng, Wang Zequan, Zhu Zijiang
Department of Clinical Medicine, Gansu University of Traditional Chinese Medicine.
Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, China.
Medicine (Baltimore). 2020 Feb;99(7):e19114. doi: 10.1097/MD.0000000000019114.
Thoracic diseases include a variety of common human primary malignant tumors, among which lung cancer and esophageal cancer are among the top 10 in cancer incidence and mortality. Early diagnosis is an important part of cancer treatment, so artificial intelligence (AI) systems have been developed for the accurate and automated detection and diagnosis of thoracic tumors. However, the complicated AI structure and image processing made the diagnosis result of AI-based system unstable. The purpose of this study is to systematically review published evidence to explore the accuracy of AI systems in diagnosing thoracic cancers.
We will conduct a systematic review and meta-analysis of the diagnostic accuracy of AI systems for the prediction of thoracic diseases. The primary objective is to assess the diagnostic accuracy of thoracic cancers, including assessing potential biases and calculating combined estimates of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The secondary objective is to evaluate the factors associated with different models, classifiers, and radiomics information. We will search databases such as PubMed/MEDLINE, Embase (via OVID), and the Cochrane Library. Two reviewers will independently screen titles and abstracts, perform full article reviews and extract study data. We will report study characteristics and assess methodological quality using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. RevMan 5.3 and Meta-disc 1.4 software will be used for data synthesis. If pooling is appropriate, we will produce summary receiver operating characteristic (SROC) curves, summary operating points (pooled sensitivity and specificity), and 95% confidence intervals around the summary operating points. Methodological subgroup and sensitivity analyses will be performed to explore heterogeneity.
CRD42019135247.
胸部疾病包括多种常见的人类原发性恶性肿瘤，其中肺癌和食管癌在癌症发病率和死亡率中位列前十。早期诊断是癌症治疗的重要环节，因此已开发出人工智能（AI）系统用于胸部肿瘤的准确自动检测与诊断。然而，复杂的AI结构和图像处理使得基于AI的系统诊断结果不稳定。本研究的目的是系统回顾已发表的证据，以探讨AI系统在诊断胸部癌症方面的准确性。
我们将对AI系统预测胸部疾病的诊断准确性进行系统回顾和荟萃分析。主要目标是评估胸部癌症的诊断准确性，包括评估潜在偏倚以及计算敏感性、特异性和受试者操作特征曲线下面积（AUC）的合并估计值。次要目标是评估与不同模型、分类器和放射组学信息相关的因素。我们将检索PubMed/MEDLINE、Embase（通过OVID）和Cochrane图书馆等数据库。两名评审员将独立筛选标题和摘要，进行全文评审并提取研究数据。我们将使用诊断准确性研究质量评估2（QUADAS - 2）工具报告研究特征并评估方法学质量。将使用RevMan 5.3和Meta - disc 1.4软件进行数据合成。如果合并合适，我们将生成汇总受试者操作特征（SROC）曲线、汇总操作点（合并敏感性和特异性）以及汇总操作点周围的95%置信区间。将进行方法学子组和敏感性分析以探索异质性。
PROSPERO注册号：CRD42019135247。
