Development and validation of a random forest model for predicting radiation pneumonitis in lung cancer patients receiving moderately hypofractionated radiotherapy: a retrospective cohort study

Background: Radiation pneumonitis (RP) is a type of toxicity commonly associated with thoracic radiation therapy. We sought to establish a random forest (RF) model and evaluate its ability to predict RP in patients with non-small cell lung cancer (NSCLC) receiving moderately hypofractionated radiotherapy (hypo-RT). Methods: A total of 106 patients with stage II-IVa NSCLC who received moderately hypofractionated helical tomotherapy (2.3-3.0 Gy/fraction) at Zhongshan Hospital were included. All enrolled patients were divided chronologically into the training (67 patients) and validation (39 patients) groups. Higher than or equal to grade 2 RP was defined as the end point. Logistic regression and RF models were established and compared using the receiver operating characteristic (ROC) and a confusion matrix in the training and validation groups.Results: The cumulative incidence of the end point was 25.4% and 17.9% in the training and validation groups, respectively. Logistic regression models were constructed by dosage parameters of total lungs, ipsilateral or contralateral lungs, respectively. ROC analysis revealed that the dosimetric factors of total lungs yielded a superior classification performance than did that of the ipsilateral or contralateral lungs [area under the curve (AUC) =0.920, AUC =0.701, and AUC =0.661, respectively]. Furthermore, the RF model yielded a better prediction capacity than did the traditional logistic model based on the dosimetric factors of the total lungs (accuracy: 88.06%; precision: 84.62%; sensitivity: 64.71%; specificity: 96.00%). Moreover, the RF identified mean lung dose [MLD; mean decrease gini (MDG) =5.74], V20 (MDG =4.62), and V35 (MDG =3.08) of total lungs as the most common primary differentiators of RP.Conclusions: Our RF model established based on the dosimetric parameters of the total lungs could accurately predict the RP risk in patients with NSCLC treated with moderately hypofractionated tomotherapy.