PURPOSE: Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. METHODS AND MATERIALS: Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). RESULTS: The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. CONCLUSIONS: The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6.
PURPOSE: Currently, prediction of survival for non-small-cell lung cancerpatients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. METHODS AND MATERIALS: Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancerpatients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). RESULTS: The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. CONCLUSIONS: The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6.
Authors: Jie Lin; Corey A Carter; Katherine A McGlynn; Shelia H Zahm; Joel A Nations; William F Anderson; Craig D Shriver; Kangmin Zhu Journal: J Thorac Oncol Date: 2015-12 Impact factor: 15.609
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Authors: Philippe Lambin; Ruud G P M van Stiphout; Maud H W Starmans; Emmanuel Rios-Velazquez; Georgi Nalbantov; Hugo J W L Aerts; Erik Roelofs; Wouter van Elmpt; Paul C Boutros; Pierluigi Granone; Vincenzo Valentini; Adrian C Begg; Dirk De Ruysscher; Andre Dekker Journal: Nat Rev Clin Oncol Date: 2012-11-20 Impact factor: 66.675
Authors: Lukas Käsmann; Maximilian Niyazi; Oliver Blanck; Christian Baues; René Baumann; Sophie Dobiasch; Chukwuka Eze; Daniel Fleischmann; Tobias Gauer; Frank A Giordano; Yvonne Goy; Jan Hausmann; Christoph Henkenberens; David Kaul; Lisa Klook; David Krug; Matthias Mäurer; Cédric M Panje; Johannes Rosenbrock; Lisa Sautter; Daniela Schmitt; Christoph Süß; Alexander H Thieme; Maike Trommer-Nestler; Sonia Ziegler; Nadja Ebert; Daniel Medenwald; Christian Ostheimer Journal: Strahlenther Onkol Date: 2017-10-13 Impact factor: 3.621
Authors: Mathilda L Bongers; Dirk de Ruysscher; Cary Oberije; Philippe Lambin; Carin A Uyl-de Groot; V M H Coupé Journal: Med Decis Making Date: 2015-03-02 Impact factor: 2.583
Authors: Luke A Hunter; Shane Krafft; Francesco Stingo; Haesun Choi; Mary K Martel; Stephen F Kry; Laurence E Court Journal: Med Phys Date: 2013-12 Impact factor: 4.071