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. (10th July 2018)

• Record Type: Journal Article
• Authors: Chao, Hann‐Hsiang; Valdes, Gilmer; Luna, Jose M.; Heskel, Marina; Berman, Abigail T.; Solberg, Timothy D.; Simone, Charles B.
• Is Part Of: Journal of applied clinical medical physics. Volume 19:Number 5(2018)
• Page Start: 539
• Page End: 546
• Publication Date: 2018-07-10
• Subjects: chest wall pain -- dosimetry -- machine learning -- non‐small‐cell lung cancer -- SBRT; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12415
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Background and purpose: Chest 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 methods: Twenty‐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. Results: Univariate 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. Conclusions: Using 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.