Standardised FDG uptake: A prognostic factor for inoperable non-small cell lung cancer

The aim of this study was to investigate the relationship between standardised uptake value (SUV) obtained from [18F]fluorodeoxyglucose positron emission tomography (FDG PET) and treatment response/survival of inoperable non-small cell lung cancer (NSCLC) patients treated with high dose radiotherapy. Fifty-one patients were included recording stage, performance, weight loss, tumour volume, histology, lymph node involvement, SUV, and delivered radiation dose. The maximum SUV (SUVmax) within the primary tumour was a sensitive and specific factor for predicting treatment response. Apart from SUVmax, stage and performance were also independent predictive factors for treatment response. In a multivariate disease-specific survival (DSS) analysis, SUVmax (P = 0.01), performance status (P = 0.008) and stage (P = 0.04) were prognostic factors. For overall survival (OS), SUVmax (P = 0.001) and performance (P = 0.06) were important prognostic factors. SUVmax was an important prognostic factor for survival of inoperable NSCLC patients and a predictive factor for treatment response. Although the number of patients was small, the treatment was not homogeneous and the use of FDG SUV may have had constraints, we still conclude that the FDG SUV is potentially a good indicator for selecting patients for different treatment strategies

Using generalized equivalent uniform dose atlases to combine and analyze prospective dosimetric and radiation pneumonitis data from 2 non-small cell lung cancer dose escalation protocols

PURPOSE: To demonstrate the use of generalized equivalent uniform dose (gEUD) atlas for data pooling in radiation pneumonitis (RP) modeling, to determine the dependence of RP on gEUD, to study the consistency between data sets, and to verify the increased statistical power of the combination. METHODS AND MATERIALS: Patients enrolled in prospective phase I/II dose escalation studies of radiation therapy of non-small cell lung cancer at Memorial Sloan-Kettering Cancer Center (MSKCC) (78 pts) and the Netherlands Cancer Institute (NKI) (86 pts) were included; 10 (13%) and 14 (17%) experienced RP requiring steroids (RPS) within 6 months after treatment. gEUD was calculated from dose-volume histograms. Atlases for each data set were created using 1-Gy steps from exact gEUDs and RPS data. The Lyman-Kutcher-Burman model was fit to the atlas and exact gEUD data. Heterogeneity and inconsistency statistics for the fitted parameters were computed. gEUD maps of the probability of RPS rate ≥20% were plotted. RESULTS: The 2 data sets were homogeneous and consistent. The best fit values of the volume effect parameter a were small, with upper 95% confidence limit around 1.0 in the joint data. The likelihood profiles around the best fit a values were flat in all cases, making determination of the best fit a weak. All confidence intervals (CIs) were narrower in the joint than in the individual data sets. The minimum P value for correlations of gEUD with RPS in the joint data was .002, compared with P=.01 and .05 for MSKCC and NKI data sets, respectively. gEUD maps showed that at small a, RPS risk increases with gEUD. CONCLUSIONS: The atlas can be used to combine gEUD and RPS information from different institutions and model gEUD dependence of RPS. RPS has a large volume effect with the mean dose model barely included in the 95% CI. Data pooling increased statistical power