Affiliation:
1. Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
2. Department of Medicine, Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD.
Abstract
Objectives
This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer.
Methods
Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability.
Results
Overall sensitivity was 0.748 (95% CI, 0.703–0.789). Overall specificity was 0.795 (95% CI, 0.742–0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266–0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850–4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477–17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80–0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80–0.89] and 0.85 [0.71–0.93], respectively).
Conclusions
The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Radiology, Nuclear Medicine and imaging
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献