Functional dose-volume histograms for predicting radiation pneumonitis in locally advanced non-small cell lung cancer treated with late-course accelerated hyperfractionated radiotherapy

Exp Ther Med. 2011 Sep;2(5):1017-1022. doi: 10.3892/etm.2011.301. Epub 2011 Jun 29.

Abstract

The aim of this study was to determine whether functional dose-volume histograms (FDVHs) are valuable for predicting radiation pneumonitis (RP), and to identify whether FDVHs have advantages over conventional dose-volume histograms (DVHs) for the prediction of RP in patients with locally advanced non-small cell lung cancer (LANSCLC). Fifty-seven patients with LANSCLC undergoing functional image-guided late-course accelerated hyperfractionated radiotherapy were enrolled. The grade of RP was evaluated according to the Common Toxicity Criteria 3.0. To identify predictive factors of RP, the FDVHs, including the volume of the functional lung receiving 5 Gy (FV(5)) through 50 Gy (FV(50)), mean perfusion-weighted lung dose (MPWLD) and functional normal tissue complication probability (FNTCP), were analyzed and compared to their counterparts [total lung receiving 5 Gy (V(5)) through 50 Gy (V(50)), mean lung dose (MLD) and normal tissue complication probability (NTCP)] derived from conventional DVHs. Univariate analysis revealed that V(5)-V(40), MLD, NTCP and FV(5)-FV(50), MPWLD, FNTCP were all statistically significant relative to the development of RP (all p<0.05). Multivariate analysis showed that only MLD and FV(15) were associated with RP (p=0.001 and 0.044, respectively). Receiver operator characteristic curve anaysis indicated that almost all of the FDVHs had larger areas under the curve compared to the DVHs, although no statistically significant difference was observed (p-value ranged from 0.066 to 0.951). FDVHs are valuable for predicting RP with the predictive efficiency equivalent to or slightly advantageous over conventional DVHs. More homogeneous studies involving larger numbers of patients are required to further assess the value of FDVHs for predicting RP.