Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China; The Second School of Clinical Medicine, Wuhan University, Wuhan, China.
Clin Imaging. 2024 Oct;114:110275. doi: 10.1016/j.clinimag.2024.110275. Epub 2024 Sep 2.
This study aimed to systematically assess the quality and performance of computed tomography (CT) radiomics studies in predicting brain metastasis (BM) among patients with lung cancer.
The PubMed, Embase and Web of Science were searched for studies predicting BM in patients with lung cancer using CT-based radiomics features. Information regarding patients, imaging, and radiomics analysis was extracted from eligible studies. We assessed the quality of included studies using the Radiomics Quality Scoring (RQS) tool and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A meta-analysis of studies regarding the prediction of BM in patients with lung cancer was performed.
Thirteen studies were identified, with sample sizes ranging from 75 to 602. The mean RQS of the studies was 12 (range 9-16), and the corresponding percentage of the score was 33.55 % (range 25.00-44.44 %). Four studies (30.8 %) were considered as low risk of bias, while the remaining nine studies (69.2 %) were considered to have unclear risks. The meta-analysis included twelve studies. The pooled sensitivity, specificity and Area Under the Curve (AUC) value with 95 % confidence intervals were 0.75 [0.69, 0.80], 0.76 [0.68, 0.82], and 0.81 [0.77-0.84], respectively.
CT radiomics-based models show promising results as a non-invasive method to predict BM in lung cancer patients. However, multicenter and prospective studies are warranted to enhance the stability and acceptance of radiomics.
本研究旨在系统评估基于计算机断层扫描（CT）的放射组学预测肺癌患者脑转移（BM）的研究的质量和性能。
在 PubMed、Embase 和 Web of Science 上检索了使用 CT 基于放射组学特征预测肺癌患者 BM 的研究。从合格研究中提取有关患者、成像和放射组学分析的信息。我们使用放射组学质量评分（RQS）工具和诊断准确性研究的质量评估（QUADAS-2）评估纳入研究的质量。对有关肺癌患者 BM 预测的研究进行了荟萃分析。
确定了 13 项研究，样本量从 75 到 602 不等。研究的平均 RQS 为 12（范围 9-16），相应的分数百分比为 33.55%（范围 25.00-44.44%）。四项研究（30.8%）被认为偏倚风险低，而其余九项研究（69.2%）被认为偏倚风险不确定。荟萃分析包括 12 项研究。汇总的敏感性、特异性和 95%置信区间的曲线下面积（AUC）值分别为 0.75[0.69,0.80]、0.76[0.68,0.82]和 0.81[0.77-0.84]。
基于 CT 放射组学的模型显示出作为一种非侵入性方法预测肺癌患者 BM 的有前途的结果。然而，需要多中心和前瞻性研究来提高放射组学的稳定性和可接受性。
