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
JournalJournal of Applied Clinical Medical Physics
PublisherJohn Wiley and Sons Ltd
MetadataShow full item record
AbstractBackground 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. Copyright 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
Identifier to cite or link to this itemhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85050507532&doi=10.1002%2facm2.12415&partnerID=40&md5=e8da56574dae7638dcb850b08c5c1170; http://hdl.handle.net/10713/9294
- Effect of Tumor Location and Dosimetric Predictors for Chest Wall Toxicity in Single-Fraction Stereotactic Body Radiation Therapy for Stage I Non-Small Cell Lung Cancer.
- Authors: Manyam BV, Videtic GMM, Verdecchia K, Reddy CA, Woody NM, Stephans KL
- Issue date: 2019 Mar
- Assessment of Monte Carlo algorithm for compliance with RTOG 0915 dosimetric criteria in peripheral lung cancer patients treated with stereotactic body radiotherapy.
- Authors: Pokhrel D, Sood S, Badkul R, Jiang H, McClinton C, Lominska C, Kumar P, Wang F
- Issue date: 2016 May 8
- Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.
- Authors: Valdes G, Solberg TD, Heskel M, Ungar L, Simone CB 2nd
- Issue date: 2016 Aug 21
- Low incidence of chest wall pain with a risk-adapted lung stereotactic body radiation therapy approach using three or five fractions based on chest wall dosimetry.
- Authors: Coroller TP, Mak RH, Lewis JH, Baldini EH, Chen AB, Colson YL, Hacker FL, Hermann G, Kozono D, Mannarino E, Molodowitch C, Wee JO, Sher DJ, Killoran JH
- Issue date: 2014
- Predicting chest wall pain from lung stereotactic body radiotherapy for different fractionation schemes.
- Authors: Woody NM, Videtic GM, Stephans KL, Djemil T, Kim Y, Xia P
- Issue date: 2012 May 1