Literature DB >> 20472276

Predictive value of modeled AUC(AFP-hCG), a dynamic kinetic parameter characterizing serum tumor marker decline in patients with nonseminomatous germ cell tumor.

Benoit You1, Ludivine Fronton, Helen Boyle, Jean-Pierre Droz, Pascal Girard, Brigitte Tranchand, Benjamin Ribba, Michel Tod, Sylvie Chabaud, Henri Coquelin, Aude Fléchon.   

Abstract

OBJECTIVE: The early decline profile of alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG) in patients with nonseminomatous germ cell tumors (NSGCT) treated with chemotherapy may be related to the risk of relapse. We assessed the predictive values of areas under the curve of hCG (AUC(hCG)) and AFP (AUC(AFP)) of modeled concentration-time equations on progression-free survival (PFS).
METHODS: Single-center retrospective analysis of hCG and AFP time-points from 65 patients with IGCCCG intermediate-poor risk NSGCT treated with 4 cycles of bleomycin-etoposide-cisplatin (BEP). To determine AUC(hCG) and AUC(AFP) for D0-D42, AUCs for D0-D7 were calculated using the trapezoid rule and AUCs for D7-D42 were calculated using the mathematic integrals of equations modeled with NONMEM. Combining AUC(AFP) and AUC(hCG) enabled us to define 2 predictive groups: namely, patients with favorable and unfavorable AUC(AFP-hCG). Survival analyses and ROC curves assessed the predictive values of AUC(AFP-hCG) groups regarding progression-free survival (PFS) and compared them with those of half-life (HL) and time-to-normalization (TTN).
RESULTS: Mono-exponential models best fit the patterns of marker decreases. Patients with a favorable AUC(AFP-hCG) had a significantly better PFS (100% vs 71.5%, P = .014). ROC curves confirmed the encouraging predictive accuracy of AUC(AFP-hCG) against HL or TTN regarding progression risk (ROC AUCs = 79.6 vs 71.9 and 70.2 respectively). Because of the large number of patients with missing data, multivariate analysis could not be performed.
CONCLUSION: AUC(AFP-hCG) is a dynamic parameter characterizing tumor marker decline in patients with NSGCT during BEP treatment. Its value as a promising predictive factor should be validated. Copyright 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20472276     DOI: 10.1016/j.urology.2010.02.049

Source DB:  PubMed          Journal:  Urology        ISSN: 0090-4295            Impact factor:   2.649


  5 in total

1.  [The role of tumour markers in diagnosis and management of testicular germ cell tumours].

Authors:  S Krege; P Albers; A Heidenreich
Journal:  Urologe A       Date:  2011-03       Impact factor: 0.639

2.  Validation of an online tool for early prediction of the failure-risk in gestational trophoblastic neoplasia patients treated with methotrexate.

Authors:  Kathleen Dekeister; Pierre-Adrien Bolze; Michel Tod; Rémi Tod; Jérôme Massardier; Jean-Pierre Lotz; Touria Hajri; Olivier Colomban; Michael J Seckl; Ray Osborne; Gilles Freyer; François Golfier; Benoit You
Journal:  Cancer Chemother Pharmacol       Date:  2020-06-04       Impact factor: 3.333

Review 3.  A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology.

Authors:  Kyungsoo Park
Journal:  Yonsei Med J       Date:  2017-01       Impact factor: 2.759

4.  Revisiting myocardial necrosis biomarkers: assessment of the effect of conditioning therapies on infarct size by kinetic modelling.

Authors:  David Ternant; Fabrice Ivanes; Fabrice Prunier; Nathan Mewton; Theodora Bejan-Angoulvant; Gilles Paintaud; Michel Ovize; Denis Angoulvant
Journal:  Sci Rep       Date:  2017-09-06       Impact factor: 4.379

Review 5.  Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response.

Authors:  Brendan C Bender; Emilie Schindler; Lena E Friberg
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

  5 in total

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