Interpretation of increments in serial tumour biomarker concentrations depends on the distance of the baseline concentration from the cut-off.
BACKGROUND
In the management of breast cancer, several algorithms for interpretation of measured concentrations during monitoring have been introduced, without objective evaluation of their performance regarding the distance of the starting concentration from the cut-off concentration.


METHODS
A computer simulation model has developed using parameters for the tumour biomarker CA 15-3 regarding biological variation and different rates of increase during progressive disease. Seven different algorithms, which include the cut-off point in the calculations, are applied to the simulated data corresponding to 1000 surrogate patients. Steady-state variation (CV-within-subject=14.9%) is based on Gaussian random numbers and an exponential increase in tumour growth (λ=0.009, 0.021, and 0.090 kU/day).


RESULTS
The main outcome of the simulations was that low starting concentrations (baseline concentrations) delay the detection of progressive disease (true-positive), whereas, baseline concentrations just below the cut-off value results in false-positive results of progression during steady-state situations. The algorithms investigated show varying susceptibility for baseline concentrations approaching the cut-off. Thus, three of the algorithms show > 90% false-positives and three algorithms < 5% false-positives when baseline concentrations were just below the cut-off point.


CONCLUSIONS
Based on the simulations, it is possible to select the optimal algorithms for early detection of progressive disease and a low percentage of false-positives.
Norwegian Quality Improvement of Primary Care Laboratories (NOKLUS), Section for General Practice, University of Bergen, Bergen, Norway. Per.Petersen@isf.uib.no
Per Hyltoft
PH
Petersen
György
G
Sölétormos
Merete Frejstrup
MF
Pedersen
Flemming
F
Lund
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000378:metabolism; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D018396:Mucin-1 / Q000378:metabolism; D013997:Time Factors
D014408:Biomarkers, Tumor; D018396:Mucin-1