Extracting the normal lung dose–response curve from clinical DVH data: a possible role for low dose hyper-radiosensitivity, increased radioresistance

Abstract

In conventionally fractionated radiation therapy for lung cancer, radiation pneumonitis’ (RP) dependence on the normal lung dose-volume histogram (DVH) is not well understood. Complication models alternatively make RP a function of a summary statistic, such as mean lung dose (MLD). This work searches over damage profiles, which quantify sub-volume damage as a function of dose. Profiles that achieve best RP predictive accuracy on a clinical dataset are hypothesized to approximate DVH dependence. Step function damage rate profiles R(D) are generated, having discrete steps at several dose points. A range of profiles is sampled by varying the step heights and dose point locations. Normal lung damage is the integral of R(D) with the cumulative DVH. Each profile is used in conjunction with a damage cutoff to predict grade 2 plus (G2+) RP for DVHs from a University of Michigan clinical trial dataset consisting of 89 CFRT patients, of which 17 were diagnosed with G2+ RP. Optimal profiles achieve a modest increase in predictive accuracy— erroneous RP predictions are reduced from 11 (using MLD) to 8. A novel result is that optimal profiles have a similar distinctive shape: enhanced damage contribution from low doses (<20 Gy), a flat contribution from doses in the range ~20–40 Gy, then a further enhanced contribution from doses above 40 Gy. These features resemble the hyper-radiosensitivity / increased radioresistance (HRS/IRR) observed in some cell survival curves, which can be modeled using Joiner’s induced repair model. A novel search strategy is employed, which has the potential to estimate RP dependence on the normal lung DVH. When applied to a clinical dataset, identified profiles share a characteristic shape, which resembles HRS/IRR. This suggests that normal lung may have enhanced sensitivity to low doses, and that this sensitivity can affect RP risk.

Bioinformatics Analysis for Identifying Differentially Expressed MicroRNAs Derived from Plasma Exosomes Associated with Radiotherapy Resistance in Non-Small-Cell Lung Cancer

“…Dawe et al [ 6 ] reported that radiotherapy demonstrated unsatisfactory outcomes, with a median survival of 10 months and 5-year survival of only 5% in stage III NSCLC patients. Other studies have shown that the local recurrence rate of NSCLC could be up to 60-70% after conventionally fractionated radiotherapy [ 7 , 8 ]. Further, patients with radioresistant tumors are at high risk for recurrence and usually have very poor prognoses [ 9 ].…”

Plasma miRNAs in predicting radiosensitivity in non-small cell lung cancer

“…Radioresistance is a major bottleneck in the treatment of tumor and its relapse [4]. The local recurrence rate in NSCLC is up to 60–70 % in 2 years after conventional fractionated radiotherapy (CFRT) (60 Gy/30 F/6 W) [5, 6]. Therefore, it is very critical to enhance the radiosensitivity of lung cancer so as to reduce the relapse.…”

On the feasibility of extracting dose–response curves from clinical DVH data using correlation and regression analysis

“…2.1. University of Michigan DVH data UM data is reported in detail by Kong [4] (see also prior publications cited by Kong), and has been previously analyzed in Gordon et al [8,9]. In summary, the UM dataset consists of 89 DVHs for total lung minus gross tumor volume.…”

“…Recent measurements [13,14] suggest that the α/β ratio for normal lung is lower than 3 Gy, motivating the value α/β=2 Gy. Based on Gordon et al [8] selected IRM model parameters are α s =0.04, α r =0.0032, D c =0.22, β=0.0032, and α r /β=1. The default assumption in this work is that the number of fractions N=30.…”

“…The LQ model is a generally accepted model of radiation-induced cell damage. Motivation for the alternative IRM model is given by Gordon et al [8,9]. For the LKB model, parameter fitting could be done in different ways.…”