Interpretation of Lung Cancer Plasma EGFR Mutation Tests in the Clinical Setting: An Update With Meta-Analysis. Issue 2 (27th December 2022)

• Record Type: Journal Article
• Authors: Cheung, Alvin Ho-Kwan; Wong, Kit-Yee; Chiang, Cho-Han; Liu, Xiaoli; Zhang, Yihan; Hui, Chris Ho-Lam; Chen, Bonan; Wang, Yifei; Chow, Chit; Kang, Wei; To, Ka-Fai
• Is Part Of: American journal of clinical pathology. Volume 159:Issue 2(2023)
• Page Start: 181
• Page End: 191
• Publication Date: 2022-12-27
• Subjects: Liquid biopsy -- Plasma EGFR testing -- Lung cancer; Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqac149
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Objectives: Comprehensive data synthesis of the clinical parameters that affect plasma EGFR mutation test results in non–small cell lung carcinoma is lacking. Although individual studies have suggested a variety of patient characteristics that can affect diagnostic accuracy, no unified conclusion has been reached. Methods: We analyzed 170 plasma EGFR mutation tests performed between 2015 and 2021 at our institution and carried out a systematic review and meta-analysis to identify clinical and imaging features that correlate with plasma EGFR mutation test sensitivity. Results: Data synthesis from 14 studies of 2, 576 patients revealed that patients with stage IV disease had a significantly lower false-negative rate than those with stage I through III disease. For our institutional cohort, which consisted of 75 paired plasma and tissue tests that were assessable for diagnostic accuracy, the overall sensitivity was 70.59% (95% confidence interval, 56.17%-82.51%). Patients who had distant metastases and more suspicious lymph nodes on imaging findings correlated with a low false-negative rate. Conclusions: While interpreting plasma EGFR mutation results, extra caution should be exercised for patients with early-stage, localized disease to accommodate the possibility of false-negative results. These meta-analyses and clinical data may enable clinicians to make evidence-based judgments for individual patients.