Background: Circulating tumor DNA (ctDNA) has offered a minimally invasive and feasible approach for detection of EGFR mutation for non-small cell lung cancer (NSCLC). This meta-analysis was designed to investigate the diagnostic value of ctDNA, compared with current "gold standard," tumor tissues.
Methods: We searched PubMed, EMBASE, Cochrane Library, and Web of Science to identify eligible studies that reported the sensitivity and specificity of ctDNA for detection of EGFR mutation status in NSCLC. Eligible studies were pooled to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR). The summary ROC curve (SROC) and area under SROC (AUSROC) were used to evaluate the overall diagnostic performance.
Results: Twenty-seven eligible studies involving 3,110 participants were included and analyzed in our meta-analysis, and most studies were conducted among Asian population. The pooled sensitivity, specificity, and DOR were 0.620 [95% confidence intervals (CI), 0.513-0.716), 0.959 (95% CI, 0.929-0.977), and 38.270 (95% CI, 21.090-69.444), respectively. The AUSROC was 0.91 (95% CI, 0.89-0.94), indicating the high diagnostic performance of ctDNA.
Conclusion: ctDNA is a highly specific and effective biomarker for the detection of EGFR mutation status.
Impact: ctDNA analysis will be a key part of personalized cancer therapy of NSCLC.
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http://dx.doi.org/10.1158/1055-9965.EPI-14-0895 | DOI Listing |
Nephrol Nurs J
January 2025
Professor of Medicine, Department of Internal Medicine, Division of Nephrology, School of Medicine, Virginia Commonwealth University.
Chronic kidney disease (CKD) affects 10% of the global population, with increasing prevalence driven by diabetes, hypertension, and aging populations. CKD often progresses asymptomatically, frequently undetected until advanced stages, and may require costly treatments, such as dialysis or transplantation. CKD imposes a substantial financial burden on health care systems, with management costs rising sharply as the disease progresses, underscoring the need for early, cost-effective interventions.
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January 2025
Shree S K Patel College of Pharmaceutical Education and Research, Ganpat University, Mahesana, Gujarat, 384012, India.
Therapeutic hurdles persist in the fight against lung cancer, although it is a leading cause of cancer-related deaths worldwide. Results are still not up to par, even with the best efforts of conventional medicine, thus new avenues of investigation are required. Examining how immunotherapy, precision medicine, and AI are being used to manage lung cancer, this review shows how these tools can change the game for patients and increase their chances of survival.
View Article and Find Full Text PDFEcancermedicalscience
November 2024
Instituto Venezolano de Investigaciones Científicas (IVIC), Unidad de Estudios Genéticos y Forenses (UEGF), Caracas 1020, República Bolivariana de Venezuela.
Colorectal cancer (CRC) is the third most commonly occurring cancer in men and the second most commonly occurring cancer in women. The epidermal growth factor receptor (EGFR) is relevant in the development and progression of CRC, because it is part of multiple signaling pathways involved in processes of the cell cycle, their malfunction causes dysregulation and subsequently carcinogenesis. Consequently, therapies were developed with anti-EGFR monoclonal antibodies (MAbs) that improve the survival of patients with CRC.
View Article and Find Full Text PDFJ Pathol Transl Med
January 2025
Department of Forensic Medicine, Pusan National University School of Medicine, Yangsan, Korea.
Background: Epidermal growth factor receptor (EGFR) gene mutation testing is crucial for the administration of tyrosine kinase inhibitors to treat non-small cell lung cancer. In addition to traditional tissue-based tests, liquid biopsies using plasma are increasingly utilized, particularly for detecting T790M mutations. This study compared tissue- and plasma-based EGFR testing methods.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Background: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.
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