Radiomic and dosimetric parameter-based nomogram predicts radiation esophagitis in patients with non-small cell lung cancer undergoing combined immunotherapy and radiotherapy

Abstract

Background: The combination of immune checkpoint inhibitors (ICIs) and radiotherapy (RT) may increase the risk of radiation esophagitis (RE). This study aimed to establish and validate a new nomogram to predict RE in patients with non-small cell lung cancer (NSCLC) undergoing immunochemotherapy followed by RT (ICI-RT).

Methods: The 102 eligible patients with NSCLC treated with ICI-RT were divided into training (n = 71) and validation (n = 31) cohorts. Clinicopathologic features, dosimetric parameters, inflammatory markers, and radiomic score (Rad-score) were included in the univariate logistic regression analysis, and factors with p < 0.05 in the univariate analysis were included in the multivariate logistic regression analysis. Factors with significant predictive values were obtained and used for developing the nomogram. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve were used to validate the model.

Results: A total of 38 (37.3%) patients developed RE. Univariate and multivariate analyses identified the following independent predictors of RE: a maximum dose delivered to the esophagus >58.4 Gy, a mean esophagus dose >13.3 Gy, and the Rad-score. The AUCs of the nomogram in the training and validation cohorts were 0.918 (95% confidence interval [CI]: 0.824-1.000) and 0.833 (95% CI: 0.697-0.969), respectively, indicating good discrimination. The calibration curves showed good agreement between the predicted occurrence of RE and the actual observations. The decision curve showed a satisfactory positive net benefit at most threshold probabilities, suggesting a good clinical effect.

Conclusions: We developed and validated a nomogram based on imaging histological features and RT dosimetric parameters. This model can effectively predict the occurrence of RE in patients with NSCLC treated using ICI-RT.

Keywords: immunotherapy; non-small-cell lung cancer; radiation esophagitis; radiomics; radiotherapy.

Figure 1

Flow chart for patient inclusion.

Figure 2

Radiomic feature selection using the LASSO regression model. (A) The LASSO regression model identified radiomic features with nonzero coefficients predicting RE. (B) Distribution of LASSO regression coefficients for predicting radiomic characteristics of RE. (C) Optimal combination of radiomic features and their correlation coefficients for predicting RE. LASSO, least absolute shrinkage and selection operator; RE, radiation esophagitis.

Figure 3

Correlation between the dosimetric parameters Vn (Spearman coefficient). The darker the red color, the stronger the positive correlation. V₂₀, percent of esophagus volume receiving ≥ 20 Gy; V₃₀, percent of esophagus volume receiving ≥ 30 Gy; V₄₀, percent of esophagus volume receiving ≥ 40 Gy; V₅₀, percent of esophagus volume receiving ≥ 50 Gy; V₆₀, percent of esophagus volume receiving ≥ 60 Gy.

Figure 4

Nomograms used for predicting RE in the training cohort. Nomogram incorporating the Dmax, MED, and Rad-score from patients with NSCLC. RE, radiation esophagitis; Dmax, maximum dose; MED, mean esophageal dose; Rad-score, radiomic score; NSCLC, non-small cell lung cancer.

Figure 5

ROC, calibration curves, and decision curves for nomograms predicting RE in the training cohort and validation cohort. (A) ROC curves for the nomograms for the training cohort. (B) Calibration curves for the nomograms for the training cohort. The x and y axes represent the predicted and actual probabilities, respectively. (C) Decision curves for the nomograms for the training cohort. The x and y axes represent the threshold probability and net benefit, respectively. (D) ROC curves for the validation cohort nomogram. (E) Calibration curves for the validation cohort nomogram. The x and y axes represent the predicted and actual probabilities, respectively. (F) Decision curves for the validation cohort nomogram. The x and y axes represent the threshold probability and net benefit, respectively. ROC, receiver operating characteristic; RE, radiation esophagitis.