Modeling the risk of radiation-induced acute esophagitis for combined Washington University and RTOG trial 93-11 lung cancer patients

Abstract

Purpose: To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non-small-cell lung cancer.

Methods and materials: The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemotherapy, fraction size). A wide range of dose-volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUD (generalized equivalent uniform dose) values.

Results: The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior-inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 MED+1.50 ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001).

Conclusions: Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.

Fig. 1

Univariate correlation of acute esophagitis (AE) events with dosimetric variable Dx of esophagus for the combined dataset and each subset. Dx is the minimum dose to the X% volume receiving the highest dose. Note: Although the WUSTL dataset does not show a strong x-value preference, hotter dose values to small volumes (small × values of Dx) have higher correlations for the RTOG dataset.

Fig. 2

Univariate Spearman’s coefficient correlation of acute esophagitis (AE) events with the dosimetric variable Vx of esophagus for the combined dataset and each subset. Vx is the percent volume receiving at least × dose (x has units of Gy).

Fig. 3

Univariate Spearman’s coefficient correlation of acute esophagitis (AE) events with the dosimetric variable MOHx of esophagus for the combined dataset and each subset. MOHx is the mean dose of the hottest x% of esophagus. Note: hotter dose values to small volumes (small × values of MOHx) have higher correlations for the RTOG dataset.

Fig. 4

Estimated dose response curves for Grade 2 or greater acute esophagitis, with and without concurrent chemotherapy (error bars represent the 95% confidence intervals of the estimated curve).

Fig. 5

Predicted rate of AE vs. observed rates for patients binned by predicted risk. Patients are binned according to predicted risk of AE by the two-variable model (MED, and ConChemo) with equal patient numbers in each bin. The mean predicted and observed event rates in each bin are (risk, events/patients): (0.0486, 1/62), (0.0835, 2/62), (0.1773, 19/62), (0.3047, 19/62), (0.5184, 29/62), (0.7720, 48/62). Good calibration and the large difference in risk between the high-risk and low-risk patients indicate that this might be a useful clinical model.

Fig. 6

Scatter plots of the mean esophageal dose, for patients who did or did not have concurrent chemotherapy. Scatter points are laterally perturbed to avoid visual overcrowding.

Fig. 7

Receiver operating characteristic curve based on the best two-variable logistic regression model, a function of mean esophagus dose and concurrent chemotherapy, with area under the curve of 0.83. This demonstrates a promising ability to separate patients into those with and without acute esophagitis (AE) risk.