School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
Department of Radiology, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran.
J Med Imaging Radiat Sci. 2024 Dec;55(4):101746. doi: 10.1016/j.jmir.2024.101746. Epub 2024 Sep 13.
Lung cancer's high prevalence and invasiveness make it a major global health concern. The Ki-67 index, which indicates cellular proliferation, is crucial for assessing lung cancer aggressiveness. Radiomics, which extracts quantifiable features from medical images using algorithms, may provide insights into tumor behavior. This systematic review and meta-analysis evaluate the effectiveness of radiomics in predicting Ki-67 status in Non-Small Cell Lung Cancer (NSCLC) using CT scans.
A comprehensive search was conducted in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception until April 19, 2024. Original studies discussing the performance of CT-based radiomics for predicting Ki-67 status in NSCLC cohorts were included. The quality assessment involved quality assessment of diagnostic accuracy studies (QUADAS-2), radiomics quality score (RQS) and METhodological RadiomICs Score (METRICS). Quantitative meta-analysis, using R, assessed pooled diagnostic odds ratio, sensitivity, and specificity in NSCLC cohorts.
We identified 10 studies that met the inclusion criteria, involving 2279 participants, with 9 of these studies included in quantitative meta-analysis. The pooled sensitivity and specificity of radiomics-based models for predicting Ki-67 status in NSCLC were 0.783 (95 % CI: 0.732 - 0.827) and 0.796 (95 % CI: 0.707 - 0.864) in training cohorts, and 0.803 (95 % CI: 0.744 - 0.851) and 0.696 (95 % CI: 0.613 - 0.768) in validation cohorts. It was identified in subgroup analysis that utilizing ITK-SNAP as a segmentation software contributed to a significantly higher pooled sensitivity.
This meta-analysis indicates promising diagnostic accuracy of radiomics in predicting Ki-67 in NSCLC.
肺癌的高患病率和侵袭性使其成为一个主要的全球健康关注点。Ki-67 指数是评估肺癌侵袭性的关键指标，它反映了细胞的增殖情况。放射组学使用算法从医学图像中提取可量化的特征，它可能为肿瘤的行为提供深入的了解。本系统综述和荟萃分析评估了使用 CT 扫描评估 Ki-67 状态的放射组学在非小细胞肺癌 (NSCLC) 中的有效性。
从建库开始到 2024 年 4 月 19 日，我们在 PubMed/MEDLINE、Embase、Scopus 和 Web of Science 数据库中进行了全面检索。纳入了讨论使用 NSCLC 队列中的 CT 基于放射组学预测 Ki-67 状态的性能的原始研究。质量评估包括诊断准确性研究的质量评估 (QUADAS-2)、放射组学质量评分 (RQS) 和方法学放射组学评分 (METRICS)。使用 R 进行定量荟萃分析，评估 NSCLC 队列中汇总的诊断优势比、敏感性和特异性。
我们确定了 10 项符合纳入标准的研究，涉及 2279 名参与者，其中 9 项研究纳入了定量荟萃分析。放射组学模型预测 NSCLC 中 Ki-67 状态的汇总敏感性和特异性在训练队列中分别为 0.783(95%CI: 0.732-0.827)和 0.796(95%CI: 0.707-0.864)，在验证队列中分别为 0.803(95%CI: 0.744-0.851)和 0.696(95%CI: 0.613-0.768)。亚组分析表明，使用 ITK-SNAP 作为分割软件有助于显著提高汇总敏感性。
本荟萃分析表明，放射组学在预测 NSCLC 中的 Ki-67 方面具有有前景的诊断准确性。
