A nomogram to predict radiation pneumonitis, derived from a combined analysis of RTOG 9311 and institutional data

Abstract

Purpose: To test the Washington University (WU) patient dataset, analysis of which suggested that superior-to-inferior tumor position, maximum dose, and D35 (minimum dose to the hottest 35% of the lung volume) were valuable to predict radiation pneumonitis (RP), against the patient database from Radiation Therapy Oncology Group (RTOG) trial 9311.

Methods and materials: The entire dataset consisted of 324 patients receiving definitive conformal radiotherapy for non-small-cell lung cancer (WU = 219, RTOG 9311 = 129). Clinical, dosimetric, and tumor location parameters were modeled to predict RP in the individual datasets and in a combined dataset. Association quality with RP was assessed using Spearman's rank correlation (r) for univariate analysis and multivariate analysis; comparison between subgroups was tested using the Wilcoxon rank sum test.

Results: The WU model to predict RP performed poorly for the RTOG 9311 data. The most predictive model in the RTOG 9311 dataset was a single-parameter model, D15 (r = 0.28). Combining the datasets, the best derived model was a two-parameter model consisting of mean lung dose and superior-to-inferior gross tumor volume position (r = 0.303). An equation and nomogram to predict the probability of RP was derived using the combined patient population.

Conclusions: Statistical models derived from a large pool of candidate models resulted in well-tuned models for each subset (WU or RTOG 9311), which did not perform well when applied to the other dataset. However, when the data were combined, a model was generated that performed well on each data subset. The final model incorporates two effects: greater risk due to inferior lung irradiation, and greater risk for increasing normal lung mean dose. This formula and nomogram may aid clinicians during radiation treatment planning for lung cancer.

Figure 1

Definitions of Vx and Dx on the dose volume histogram.

Figure 2

Changes in Spearman's correlation coefficient as the ‘a’ parameter in the GEUD equation (= 1/n in the LKB NTCP model) is changed. The label ‘combined mixed’ refers to including all the grades in the rank correlation.

Figure 3

Radiation pneumonitis events for both datasets. Circles represent the development of pneumonitis shown as the center of mass location on an AP view of the chest. Inferior location is significantly associated with RP in the WUSTL patients. This is suggested in the 9311 patients, though does not reach statistical significance.

Figure 4

Non-pneumonitis and pneumonitis events scored by maximum PTV dose and V20. The RTOG 9311 patients received higher doses to lower V20 values than did WUSTL patients.

Figure 5

Bootstrap multimetric modeling for the combined WUSTL and RTOG 9311 population. The most frequently selected model is a two-parameter model of MLD and COM-SI.

Figure 6

Figure 6A: A nomogram to predict the incidence of radiation pneumonitis (NTCP, middle line) based on the mean lung dose (left) and the inferior-to-superior (0-1) (right) position of the center of mass. The NTCP value is obtained by connecting the MLD and tumor position with a straight line. Figure 6A contains an example. The nomogram is based on delivered doses using 3DCRT, not IMRT. Figure 6B: Same as Figure 6A, without the example Figure 6C: An accompanying plot of COM-SI according to MLD showing the number of patients (shaded area) for each combination of the two parameters; MLD and COM S-I position. No data exist for the extreme scenarios within the nomogram (i.e. COM-SI locations between 0 – 0.2 and between 0.8 – 1; MLD locations under 6 Gy and above 30 Gy).

Figure 6

Figure 6A: A nomogram to predict the incidence of radiation pneumonitis (NTCP, middle line) based on the mean lung dose (left) and the inferior-to-superior (0-1) (right) position of the center of mass. The NTCP value is obtained by connecting the MLD and tumor position with a straight line. Figure 6A contains an example. The nomogram is based on delivered doses using 3DCRT, not IMRT. Figure 6B: Same as Figure 6A, without the example Figure 6C: An accompanying plot of COM-SI according to MLD showing the number of patients (shaded area) for each combination of the two parameters; MLD and COM S-I position. No data exist for the extreme scenarios within the nomogram (i.e. COM-SI locations between 0 – 0.2 and between 0.8 – 1; MLD locations under 6 Gy and above 30 Gy).