Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non‐small‐cell lung cancer treated with stereotactic body radiation therapy

Abstract

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose–volume constraints.Materials and methodsTwenty‐five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out‐of‐bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning‐curve experiments, the dataset proved to be self‐consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis.

Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation

“…Given the result from our machine learning analysis that none of the 35 features analyzed performed better than a random classifier, this suggests that our currently utilized clinical, demographic, and dosimetric features could be inadequate to reliably predict radiation esophagitis. One can conclude that we are not currently collecting and capturing the appropriate features to allow a machine learning workflow to predict grade 3 radiation esophagitis, as we were successfully able to do when using machine learning to predict for pneumonitis in LA-NSCLC [41] and chest wall toxicity in early stage NSCLC [42]. As such, we encourage other investigators to explore and develop new markers directed at this toxicity.…”

Machine learning for radiation outcome modeling and prediction

“…Recently, there is a tremendous increase in the use of ML in different areas of radiation oncology, such as treatment planning optimization, segmentation, radiation physics quality assurance, contouring or image‐guided radiotherapy . In this paper, we focus on ML for radiation outcome modeling …”

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

“…Lung cancer RT may cause chest pain due to rib fracture, radiation-induced neuropathy of the intercostal nerves or nerve branches, chest wall edema, or chest wall fibrosis. However, the only study we found that specifically investigated chest pain is the one by (34). The authors utilized decision tree and RF methods to identify robust features predictive of chest wall pain in a cohort of 197 patients.…”