Clinical Rationale of Dual‐Parameter Tumor Markers Reporting Without Reliance on Normal Reference Ranges
INTRODUCTION: This review offers clinicians a novel view of tumor markers (TM). We suggest reporting the TM with two values: the TM level itself and the specific growth rate (SGR). Clinicians are well informed about the limitations of TM-based cancer screening and early recurrence detection. REVIEW OF CURRENT TM INTERPRETATION: Current TM interpretation is based merely on the TM value. TM cannot provide the information equivalent to histological data. An asymptomatic patient with rising TM is assigned to imaging procedures only if TM is found above the norm. PROPOSAL TO REPORT TM IN TWO-PARAMETER FORMAT: We offer to universally report TM as a two-parameter value: (a) TM itself; (b) specific growth rate of TM. The rate of change of TM, rather than the TM norm, is a reliable criterion for recurrence monitoring. For the moment, serial TM values of individual patients are becoming universally available in electronic format. REVIEW OF LITERATURE EXPLORING VELOCITY, DOUBLING TIME, AND SGR IN LABORATORY REPORTS AND RADIOLOGICAL ASSESSMENT: We offer medical laboratories to put into service a specific growth rate (SGR) parameter which precisely associates: (a) the current TM value; (b) the previous TM value; and (c) the time interval between. Radiologic tumor progression with SGR calculations significantly aided prognosis assessment, but the implementation of SGR in laboratory practice has not been tested. This review focuses on clinical data in which SGR could have been employed to guide therapy or to monitor for recurrence. The obvious advantages of the SGR-TM format are ease of interpretation (it indicates the percentage change in TM per day), cost-effectiveness, and mathematical consistency.
Authors
- Aleksandras Petrauskas
- Gintaras Zaleskis (ORCID: https://orcid.org/0000-0002-1880-6753)
- Tatjana Ivaškienė (ORCID: https://orcid.org/0000-0003-2064-6792)
- Daiva Gudavičienė
- Dainius Characiejus (ORCID: https://orcid.org/0000-0003-1060-5217)
- Jurate Civinskaite
Institutions
- Vilnius University (LT)
- Vilnius University of Applied Sciences (LT)
- State Research Institute Centre for Innovative Medicine (LT)
- Vilnius University Hospital Santariskiu Klinikos (LT)
Publication Details
- Journal
- Journal of Clinical Laboratory Analysis
- Published
- 2026-07-27
- DOI
- https://doi.org/10.1002/jcla.70320
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00