Cut-off-independent Tumour Marker Evaluation Using ROC Approximation
Background: The analysis of tumour markers is based on the evaluation of data in relation to defined cut-off values. Changes in the method of determination or reference study group have led to different results. Cut-off-independent diagnostic evaluation of laboratory parameters can avoid laboratory-...
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Veröffentlicht in: | Anticancer research 2007-11, Vol.27 (6C), p.4305-4310 |
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Sprache: | eng |
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Zusammenfassung: | Background: The analysis of tumour markers is based on the evaluation of data in relation to defined cut-off values. Changes
in the method of determination or reference study group have led to different results. Cut-off-independent diagnostic evaluation
of laboratory parameters can avoid laboratory-based and method-derived systematic errors. The decision guarantee (DG) is an
appropriate parameter that can be determined using a defined reference population and its respective receiver operating characteristic
(ROC) curve. The influence of ROC differences on the determination of DG is examined. Patients and Methods: A group of 281
consecutive patients with newly diagnosed, histologically confirmed lung cancer and a control group of 231 patients were examined.
Histological classification of the tumour cases defined in 59 small-cell carcinoma, 102 squamous cell carcinomas, 66 adenocarcinomas
and 54 large-cell carcinomas or mixed bronchial carcinomas without classification. The control group without tumours consisted
of 23 healthy subjects, 125 patients with silicosis or asbestosis, 27 with chronic obstructive pulmonary diseases (COPD) and
56 suffering from inflammatory lung diseases. Results: Cytokeratin-19 fragments (CYFRA 21-1) was the most sensitive marker
with a sensitivity of 57.3% and a specificity of 94.9%. Sensitivity and specificity influence each other. Related to the ROC
curve, the method described here ensured the diagnosis of lung cancer on the basis of the data collected in comparison with
a reference population. Thus, it was possible to determine with statistical certainty whether the evaluation of the sample
data would lead to a diagnosis of lung cancer. Conclusion: The DG provides the basis for a laboratory- and method-independent
support for a diagnosis including fairer information about the reference population in the data analysis. With feedback between
laboratory results and the specialist physician's comprehensive clinical findings, the sensitivity and specificity can be
continually monitored. |
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ISSN: | 0250-7005 1791-7530 |