1. Diagnostic validation and interpretation of longitudinal circulating biomarkers using a biomarker response characteristic plot.
- Author
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Moritz R, Muller M, Korse CM, van den Broek D, Baas P, van den Noort V, Ten Hoeve JJ, van den Heuvel MM, and van Rossum HH
- Subjects
- Carcinoma, Non-Small-Cell Lung blood, Humans, Lung Neoplasms blood, Antigens, Neoplasm blood, Biomarkers, Tumor blood, Carcinoembryonic Antigen blood, Carcinoma, Non-Small-Cell Lung diagnosis, Keratin-19 blood, Lung Neoplasms diagnosis, Software
- Abstract
Background: Serum-based tumor biomarkers are used to monitor cancer treatment, while clear guidance on the clinical usage is often lacking. We describe a graphical presentation to support diagnostic accuracy studies and clinical interpretation of longitudinal biomarker data., Methods: A biomarker response characteristic (BReC) plot was designed. To allow demonstration of the BReC plot application, software was developed that supported 1) dynamic generation of BReC plots, and 2) diagnostic accuracy studies of biomarker response-based medical tests. The BReC plot application was demonstrated using serial carcinoembryonic antigen (CEA) and Cyfra 21.1 results from 216 patients with metastasized non-small cell lung cancer, treated with Nivolumab in routine clinical practice., Results: The developed software supported the generation of BReC plots and diagnostic validation of biomarker response-based medical tests by generating the sensitivity, specificity and predictive values. Obtained BReC plots showed a clear relationship between clinical outcome and CEA and Cyfra 21.1 responses. Furthermore, using BReC plots, CEA and Cyfra 21.1 based medical tests were designed with a sensitivity for detection of treatment failure of 0.34 and 0.35 and a specificity of 0.96., Conclusions: The BReC plot appears to support diagnostic validation studies and the interpretation of longitudinal biomarkers though further validation is warranted., (Copyright © 2018 Elsevier B.V. All rights reserved.)
- Published
- 2018
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