PURPOSE: Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. METHODS: One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. RESULTS: Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTV(primary), p=0.002), higher pre-treatment maximum standardized uptake value (SUV(max), p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTV(primary), SUV(max), equivalent radiation dose at 2 Gy corrected for time (EQD(2, T)) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). CONCLUSION: Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from an individualized and more adequate therapeutic approach, thereby improving local control and survival.
PURPOSE: Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. METHODS: One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. RESULTS: Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTV(primary), p=0.002), higher pre-treatment maximum standardized uptake value (SUV(max), p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTV(primary), SUV(max), equivalent radiation dose at 2 Gy corrected for time (EQD(2, T)) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). CONCLUSION: Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from an individualized and more adequate therapeutic approach, thereby improving local control and survival.
Authors: Marie Wanet; Antoine Delor; François-Xavier Hanin; Benoît Ghaye; Aline Van Maanen; Vincent Remouchamps; Christian Clermont; Samuel Goossens; John Aldo Lee; Guillaume Janssens; Anne Bol; Xavier Geets Journal: Strahlenther Onkol Date: 2017-07-21 Impact factor: 3.621
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: Mitchell Machtay; Fenghai Duan; Barry A Siegel; Bradley S Snyder; Jeremy J Gorelick; Janet S Reddin; Reginald Munden; Douglas W Johnson; Larry H Wilf; Albert DeNittis; Nancy Sherwin; Kwan Ho Cho; Seok-Ki Kim; Gregory Videtic; Donald R Neumann; Ritsuko Komaki; Homer Macapinlac; Jeffrey D Bradley; Abass Alavi Journal: J Clin Oncol Date: 2013-09-16 Impact factor: 44.544
Authors: Sara Carvalho; Ralph T H Leijenaar; Emmanuel Rios Velazquez; Cary Oberije; Chintan Parmar; Wouter van Elmpt; Bart Reymen; Esther G C Troost; Michel Oellers; Andre Dekker; Robert Gillies; Hugo J W L Aerts; Philippe Lambin Journal: Acta Oncol Date: 2013-09-09 Impact factor: 4.089
Authors: Ralph T H Leijenaar; Sara Carvalho; Emmanuel Rios Velazquez; Wouter J C van Elmpt; Chintan Parmar; Otto S Hoekstra; Corneline J Hoekstra; Ronald Boellaard; André L A J Dekker; Robert J Gillies; Hugo J W L Aerts; Philippe Lambin Journal: Acta Oncol Date: 2013-09-09 Impact factor: 4.089
Authors: Ralph T H Leijenaar; Georgi Nalbantov; Sara Carvalho; Wouter J C van Elmpt; Esther G C Troost; Ronald Boellaard; Hugo J W L Aerts; Robert J Gillies; Philippe Lambin Journal: Sci Rep Date: 2015-08-05 Impact factor: 4.379