A Prediction Model to Help with Oncologic Mediastinal Evaluation for Radiation: HOMER

Abstract

Rationale: When stereotactic ablative radiotherapy is an option for patients with non-small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2|3) disease is important.Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2|3 disease.Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound-guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2|3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals.Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography-computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2|3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P < 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34).Conclusions: This prediction model can estimate the probability of N0, N1, and N2|3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

Keywords: endobronchial ultrasound; lung cancer; lung cancer staging; mediastinal adenopathy.

Figure 1.

Receiver operating characteristic curves of the prediction model in the institution of model development. The figure plots the area under the curve (AUC) for (A) N stage greater than or equal to 1 (vs. N0) disease (AUC = 0.84) and (B) N2 or N3 (vs. N0 or N1) disease (AUC = 0.85) in the development cohort, and for (C) N stage greater than or equal to 1 (vs. N0) disease (AUC = 0.86) and (D) N2 or N3 (vs. N0 or N1) disease (AUC = 0.88) in the temporal validation cohort.

Figure 2.

Observed versus predicted frequencies of the prediction model in the institution of model development. The figure plots the probability of (A) N stage greater than or equal to 1 (vs. N0) disease and (B) N2 or N3 (vs. N0 or N1) disease by decile of expected risk in the group of the development cohort, and the probability of (C) N stage greater than or equal to 1 (vs. N0) disease and (D) N2 or N3 (vs. N0 or N1) disease by decile of expected risk in the group of the temporal validation cohort. The observed probability for each decile is on the vertical axis, and the predicted probability is on the horizontal axis. A perfect model, in which observed equals predicted, is shown by the line.

Figure 3.

Observed versus predicted frequencies for combined external validation cohort. The figure plots the probability of (A) N stage greater than or equal to 1 (vs. N0) disease and (B) N2 or N3 (vs. N0 or N1) disease by decile of expected risk in that group before calibration, and the probability of (C) N stage greater than or equal to 1 (vs. N0) disease and (D) N2 or N3 (vs. N0 or N1) disease by decile of expected risk in that group after calibration. The observed probability for each decile is on the vertical axis, and the predicted probability is on the horizontal axis. A perfect model, in which observed equals predicted, is shown by the line.

Figure 4.

Observed versus predicted frequencies for each institution of the external validation cohort before calibration. The figure plots the probability of (A) N stage greater than or equal to 1 (N1|2|3) (vs. N0) disease and (B) N2 or N3 (N2|3) (vs. N0 or N1 [N0|1]) disease at the Cleveland Clinic Foundation, the probability of (C) N1|2|3 (vs. N0) disease and (D) N2|3 (vs. N0|1) disease at Johns Hopkins, and the probability of (E) N1|2|3 (vs. N0) disease and (F) N2|3 (vs. N0|1) disease at the Henry Ford Hospital. The observed probability for each decile is on the vertical axis, the predicted probability on the horizontal axis. A perfect model, in which observed equals predicted, is shown by the line.

Figure 5.

Observed versus predicted frequencies for each institution of the external validation cohort after calibration. The figure plots the probability of (A) N stage greater than or equal to 1 (N1|2|3) (vs. N0) disease and (B) N2 or N3 (N2|3) (vs. N0 or N1 [N0|1]) disease at the Cleveland Clinic Foundation, the probability of (C) N1|2|3 (vs. N0) disease and (D) N2|3 (vs. N0|1) disease at Johns Hopkins, and the probability of (E) N1|2|3 (vs. N0) disease and (F) N2|3 (vs. N0|1) disease at the Henry Ford Hospital. The observed probability for each decile is on the vertical axis, the predicted probability on the horizontal axis. A perfect model, in which observed equals predicted, is shown by the line.