Prediction of residual metabolic activity after treatment in NSCLC patients

Emmanuel Rios Velazquez*, Hugo J. W. L. Aerts, Cary Oberije, Dirk De Ruysscher, Philippe Lambin

*Corresponding author for this work

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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 postradiotherapy 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.
Original languageEnglish
Pages (from-to)1033-1039
JournalActa Oncologica
Issue number7
Publication statusPublished - Oct 2010

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