Predicting the risk of radiation-induced lung injury using a biophysical parameter

Abstract

This research work is based on the premise that many normal organs have heterogeneous volumes with spatially variable function. Consequently, the utilization of biological models that incorporate functional imaging information is imperative in predicting and quantifying the risk of radiation-induced normal organs toxicity. The objective of this work is to correlate lung toxicity data from non-small cell lung cancer (NSCLC) patients with values from the functional equivalent uniform dose biological model, so that such information may be used to predict the risk of radiation-induced lung injury. The data from 38 NSCLC patients with single photon emission computed tomography (SPECT) perfusion data, who received 3DCRT treatments at our institution, were analyzed. The patients’ dose-volume histograms were used to calculate the lung generalized equivalent uniform dose (EUD) values. The EUD reports the generalized mean value of the non-uniform dose distribution, which represents the homogenous dose distribution that produces the same lung complication as that produced by the inhomogeneous dose distribution. The patients’ dose-function histograms (DFH), which relates dose to the percentage of total function value at that dose, were used to calculate lung functional EUD (FEUD) values. The DFH incorporates lung SPECT perfusion imaging data. A treatment plan with a low lung FEUD value implies more sparing of functional sub-units and this may result in lower complications. The CT anatomical-volume of lungs was first correlated with changes in SPECT regional perfusion data. The lungs dose distributions were then evaluated using the generalized EUD and FEUD models with a lung a value of 0.96 (a or 1/n is a critical structure specific parameter which describes the dose-volume effect). The a parameter value was calculated using the tolerance dose values of Emami et al to the whole lung and partial volumes that would result in a 5% complication probability in 5 years (TD5/5). The biophysical parameter (RFE), which is the ratio of FEUD to EUD, was evaluated for each patient in order to predict the risk of radiation-induced lung pneumonitis (RP) by correlating the patients’ outcome data with the RFE values. Figure 1(left) shows the lungs FEUD and EUD data for the lung patients with and without RP. The results suggest that there is a correlation between RFE and lung RP. Twenty-three out of 32 patients without RP have FEUD values less than EUD (RFE <1). Six out of 8 patients with RP have FEUD values higher or equal to EUD (RFE ≥ 1). The RFE data quantify the radiation-induced lung toxicity (i.e. the risk of RP is higher with the irradiation of more functioning lungs) and suggest that RFE may be used as predictor for RP. Figure 2(right) shows FEUD and EUD for the lung patients with their tumor T staging. The majority of patients with tumor staging T3 and T4 have FEUD values equal or less than EUD (RFE <1). Patients with stage T1 have FEUD values approximately equal to EUD. The RFE data show that as tumor aggressiveness increases, the lung function decreases, which may be due to tumor obstruction of blood flow to the lung vessels. The biophysical parameter (RFE), which combines EUD and FEUD, quantifies radiation-induced lung injury, a fact that is not revealed by the physical EUD data alone. Furthermore, this work shows that predictions from biological models that incorporate both dose-function and dose-volume information correlate better with patient outcome data.