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The Role of Ensemble Machine Learning Algorithms to Predict Weight Loss Following Head and Neck Radiation Therapy
E384
International Journal of Radiation Oncology Biology Physics
treated after 2006 experienced longer survival across all sub-sites (13.2 vs 6 mo, interquartile ranges [6.2-28.7] vs [3.2-13.4], P < 0.01). Conclusion: Salvage surgery and re-RT following LRF are associated with improved survival. DM from OCC portends inferior outcomes compared to other sub-sites. Metastatic patients treated after 2006, when cetuximab was FDA approved for HNSCC, experienced longer survival. Understanding the natural history of recurrent/metastatic HNSCC after definitive locoregional treatment suggests several unique features which influence outcomes and may affect interpretation of clinical trials in these patients. Author Disclosure: J.E. Leeman: None. J. Li: None. P. Venigalla: None. P.B. Romesser: None. Z.S. Zumsteg: None. S.M. McBride: None. C. Tsai: None. D.S. Higginson: None. N. Katabi: None. J.O. Boyle: None. B.R. Roman: None. E.J. Sherman: None. N. Lee: Advisory Board; Merck, Pfizer, Vertex. N. Riaz: None.
methods provided higher accuracies in keeping with its known strengths in handling large and incomplete datasets. Ensemble models should be studied in greater detail to further understand the meaning of the data. Ensemble methods obtained better predictive performance than other single algorithms by allowing for a more flexible structure in the models. Diverting from the traditional CART models and other single models may be a more effective way of extracting knowledge, especially with sizeable and often incomplete datasets. Author Disclosure: A.M. Hernandez: None. Z. Cheng: None. X. Hui: None. A.P. Kiess: None. S.P. Robertson: None. J. Moore: None. M.R. Bowers: None. A. Choflet: None. J.W. Wong: None. T.R. McNutt: None. H. Quon: None. L. Burns: None. A. Thompson: None.
2946 The Role of Ensemble Machine Learning Algorithms to Predict Weight Loss Following Head and Neck Radiation Therapy A.M. Hernandez,1 Z. Cheng,2 X. Hui,3 A.P. Kiess,3 S.P. Robertson,4 J. Moore,2 M.R. Bowers,5 A. Choflet,3 J.W. Wong,3 T.R. McNutt,4 H. Quon,3 L. Burns,6 and A. Thompson6; 1George Washington University (Graduate Student), Washington, DC, 2Johns Hopkins University School of Medicine, Baltimore, MD, 3Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, MD, 4 Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 5Johns Hopkins University, Baltimore, MD, 6Johns Hopkins Hospital, BSN, Baltimore, MD Purpose/Objective(s): Head and neck cancer (HNC) patients often experience weight loss due to various radiation-related toxicities. Predicting weight loss can assist with interventions. The aim of this study was to evaluate the performance of various Machine Learning (ML) classification algorithms and determine dominant predictors for weight loss building on our prior weight loss prediction model using the Classification and Regression Trees (CART) algorithm. Materials/Methods: HNC patients receiving intensity modulated radiation therapy (RT) were queried from Oncospace. Oncospace aggregates prospective structured outcome data captured during routine clinical care with RT planning data also generated during the routine clinical workflow. Multiple ML algorithms available in the computer algorithm’s Classification Learner (CL): Decision Trees, Discriminant Analysis, Support Vector Machines, Nearest Neighbor Classifier, were used to develop prediction models for the primary end point of weight loss (5kg) at 3-month post RT, and compared to Ensemble Classifiers. The predicted response from a trained ensemble is the average of predictions from individual trees. Ten-fold cross-validation was used to protect data against over fitting. Results: From 2007-14, 326 patients with 729 variables collected during treatment and in follow up were analyzed. The primary outcome was a weight loss of (5kg), despite the use of a PEG tube. The predictors utilized with the CL were determined based on the best performance of various ‘weak learning’ ML classification algorithms (decision trees, Knearest neighbors, etc.), then compared to those with a high-quality ensemble model. The patient reported outcomes (PRO) such as “able to eat foods I like” were used with further expanding trees through Ensemble Bagged Trees and anatomic tumor location by ICD-9 resulting in a learning accuracy of 0.844. A comparison of weight loss of (5kg) with radiation dose and chemotherapy predictors resulted in 0.89 and 0.927 accuracies. Additionally, significant accuracies were also obtained for mucositis (0.862), xerostomia (0.865), T-stage (0.869), pain intensity (0.896), PEG used (0.890), and PRO “content with the quality of life” (0.902). Conclusion: ML algorithms were successfully applied modeling weight loss in our head and neck Oncospace informatics platform. Ensemble
2947 Comorbidity With Age as a Predictor of Survival for Patients With Nasopharyngeal Cancer Following Radiation Treatment: A Nationwide Population-Based Study C.C. Yang,1 P.C. Chen,2 L.C. Lin,1 S.L. Chang,3 and C.C. Lee4; 1 Department of Radiation Oncology, Chi-Mei Medical Center, Tainan, Taiwan, 2Department of Radiation Oncology, Pingtung Christian Hospital, Pingtung, Taiwan, 3Department of Otolaryngology, Chi-Mei Medical Center, Tainan, Taiwan, 4Department of Otolaryngology, Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Purpose/Objective(s): To characterize the impact of comorbidity and age on survival outcomes for patients with nasopharyngeal carcinoma (NPC) post radiation therapy (RT). Materials/Methods: A total of 4095 patients with newly diagnosed NPC treated by RT or RT plus chemotherapy (CT) in the period from 2007 to 2011 were included through Taiwan’s National Health Insurance Research Database. Comorbidities present prior to the NPC diagnosis was obtained and adapted to the Charlson Comorbidity Index (CCI) and Age-Adjusted Charlson Comorbidity Index (ACCI). Overall survival was estimated using the KaplandMeier method and the difference between groups was analyzed using log-rank test. The Cox proportional hazards regression model was used for multivariate analysis. Differences between variables with categorical data were examined using the chi-square test. Receiver Operating Characteristic (ROC) curves was generated to assess the accuracy and the predictive ability of each index for survival. Results: Most of the patients (75%) were male (age 5113 years) and 2470 of them (60%) had at least one comorbid condition. The most common comorbid condition was diabetes mellitus. According to these two different comorbidity index (CCI and ACCI), higher scores were associated with worse overall survival (P< 0.001). The Receiver Operating Characteristic (ROC) curve was used to assess the discriminating ability of CCI and AACI and it demonstrated the predictive ability for mortality with the ACCI (0.693, 95% CI 0.670-0.715) was superior to that of the CCI (0.619, 95% CI 0.593-0.644). Conclusion: Comorbidities with age greatly influenced the clinical presentations, therapeutic interventions, and outcomes of patients with NPC post RT. Higher comorbidity index scores accurately was associated with worse survival. The ACCI seems to be a more appropriate prognostic indicator and should be considered in further clinical studies. Author Disclosure: C. Yang: None. P. Chen: None. L. Lin: None. S. Chang: None. C. Lee: None.
2948 Adjuvant Radiation Therapy Improved Local Control for Primary Mucosal Melanoma of the Head and Neck M. Abugideiri,1 K. Patel,2 J.M. Switchenko,3 K. Magliocca,4 Z.S. Buchwald,1 J. Delgaudio,5 D.H. Lawson,6 H. Danish,2 J.J. Beitler,2,5 and M.K. Khan2; 1Department of Radiation Oncology at Emory University, Atlanta, GA, 2Department of Radiation Oncology, Winship Cancer Institute at Emory University, Atlanta, GA, 3Department of Biostatistics and Bioinformatics, Winship Cancer Institute at Emory
International Journal of Radiation Oncology Biology Physics
treated after 2006 experienced longer survival across all sub-sites (13.2 vs 6 mo, interquartile ranges [6.2-28.7] vs [3.2-13.4], P < 0.01). Conclusion: Salvage surgery and re-RT following LRF are associated with improved survival. DM from OCC portends inferior outcomes compared to other sub-sites. Metastatic patients treated after 2006, when cetuximab was FDA approved for HNSCC, experienced longer survival. Understanding the natural history of recurrent/metastatic HNSCC after definitive locoregional treatment suggests several unique features which influence outcomes and may affect interpretation of clinical trials in these patients. Author Disclosure: J.E. Leeman: None. J. Li: None. P. Venigalla: None. P.B. Romesser: None. Z.S. Zumsteg: None. S.M. McBride: None. C. Tsai: None. D.S. Higginson: None. N. Katabi: None. J.O. Boyle: None. B.R. Roman: None. E.J. Sherman: None. N. Lee: Advisory Board; Merck, Pfizer, Vertex. N. Riaz: None.
methods provided higher accuracies in keeping with its known strengths in handling large and incomplete datasets. Ensemble models should be studied in greater detail to further understand the meaning of the data. Ensemble methods obtained better predictive performance than other single algorithms by allowing for a more flexible structure in the models. Diverting from the traditional CART models and other single models may be a more effective way of extracting knowledge, especially with sizeable and often incomplete datasets. Author Disclosure: A.M. Hernandez: None. Z. Cheng: None. X. Hui: None. A.P. Kiess: None. S.P. Robertson: None. J. Moore: None. M.R. Bowers: None. A. Choflet: None. J.W. Wong: None. T.R. McNutt: None. H. Quon: None. L. Burns: None. A. Thompson: None.
2946 The Role of Ensemble Machine Learning Algorithms to Predict Weight Loss Following Head and Neck Radiation Therapy A.M. Hernandez,1 Z. Cheng,2 X. Hui,3 A.P. Kiess,3 S.P. Robertson,4 J. Moore,2 M.R. Bowers,5 A. Choflet,3 J.W. Wong,3 T.R. McNutt,4 H. Quon,3 L. Burns,6 and A. Thompson6; 1George Washington University (Graduate Student), Washington, DC, 2Johns Hopkins University School of Medicine, Baltimore, MD, 3Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, MD, 4 Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 5Johns Hopkins University, Baltimore, MD, 6Johns Hopkins Hospital, BSN, Baltimore, MD Purpose/Objective(s): Head and neck cancer (HNC) patients often experience weight loss due to various radiation-related toxicities. Predicting weight loss can assist with interventions. The aim of this study was to evaluate the performance of various Machine Learning (ML) classification algorithms and determine dominant predictors for weight loss building on our prior weight loss prediction model using the Classification and Regression Trees (CART) algorithm. Materials/Methods: HNC patients receiving intensity modulated radiation therapy (RT) were queried from Oncospace. Oncospace aggregates prospective structured outcome data captured during routine clinical care with RT planning data also generated during the routine clinical workflow. Multiple ML algorithms available in the computer algorithm’s Classification Learner (CL): Decision Trees, Discriminant Analysis, Support Vector Machines, Nearest Neighbor Classifier, were used to develop prediction models for the primary end point of weight loss (5kg) at 3-month post RT, and compared to Ensemble Classifiers. The predicted response from a trained ensemble is the average of predictions from individual trees. Ten-fold cross-validation was used to protect data against over fitting. Results: From 2007-14, 326 patients with 729 variables collected during treatment and in follow up were analyzed. The primary outcome was a weight loss of (5kg), despite the use of a PEG tube. The predictors utilized with the CL were determined based on the best performance of various ‘weak learning’ ML classification algorithms (decision trees, Knearest neighbors, etc.), then compared to those with a high-quality ensemble model. The patient reported outcomes (PRO) such as “able to eat foods I like” were used with further expanding trees through Ensemble Bagged Trees and anatomic tumor location by ICD-9 resulting in a learning accuracy of 0.844. A comparison of weight loss of (5kg) with radiation dose and chemotherapy predictors resulted in 0.89 and 0.927 accuracies. Additionally, significant accuracies were also obtained for mucositis (0.862), xerostomia (0.865), T-stage (0.869), pain intensity (0.896), PEG used (0.890), and PRO “content with the quality of life” (0.902). Conclusion: ML algorithms were successfully applied modeling weight loss in our head and neck Oncospace informatics platform. Ensemble
2947 Comorbidity With Age as a Predictor of Survival for Patients With Nasopharyngeal Cancer Following Radiation Treatment: A Nationwide Population-Based Study C.C. Yang,1 P.C. Chen,2 L.C. Lin,1 S.L. Chang,3 and C.C. Lee4; 1 Department of Radiation Oncology, Chi-Mei Medical Center, Tainan, Taiwan, 2Department of Radiation Oncology, Pingtung Christian Hospital, Pingtung, Taiwan, 3Department of Otolaryngology, Chi-Mei Medical Center, Tainan, Taiwan, 4Department of Otolaryngology, Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Purpose/Objective(s): To characterize the impact of comorbidity and age on survival outcomes for patients with nasopharyngeal carcinoma (NPC) post radiation therapy (RT). Materials/Methods: A total of 4095 patients with newly diagnosed NPC treated by RT or RT plus chemotherapy (CT) in the period from 2007 to 2011 were included through Taiwan’s National Health Insurance Research Database. Comorbidities present prior to the NPC diagnosis was obtained and adapted to the Charlson Comorbidity Index (CCI) and Age-Adjusted Charlson Comorbidity Index (ACCI). Overall survival was estimated using the KaplandMeier method and the difference between groups was analyzed using log-rank test. The Cox proportional hazards regression model was used for multivariate analysis. Differences between variables with categorical data were examined using the chi-square test. Receiver Operating Characteristic (ROC) curves was generated to assess the accuracy and the predictive ability of each index for survival. Results: Most of the patients (75%) were male (age 5113 years) and 2470 of them (60%) had at least one comorbid condition. The most common comorbid condition was diabetes mellitus. According to these two different comorbidity index (CCI and ACCI), higher scores were associated with worse overall survival (P< 0.001). The Receiver Operating Characteristic (ROC) curve was used to assess the discriminating ability of CCI and AACI and it demonstrated the predictive ability for mortality with the ACCI (0.693, 95% CI 0.670-0.715) was superior to that of the CCI (0.619, 95% CI 0.593-0.644). Conclusion: Comorbidities with age greatly influenced the clinical presentations, therapeutic interventions, and outcomes of patients with NPC post RT. Higher comorbidity index scores accurately was associated with worse survival. The ACCI seems to be a more appropriate prognostic indicator and should be considered in further clinical studies. Author Disclosure: C. Yang: None. P. Chen: None. L. Lin: None. S. Chang: None. C. Lee: None.
2948 Adjuvant Radiation Therapy Improved Local Control for Primary Mucosal Melanoma of the Head and Neck M. Abugideiri,1 K. Patel,2 J.M. Switchenko,3 K. Magliocca,4 Z.S. Buchwald,1 J. Delgaudio,5 D.H. Lawson,6 H. Danish,2 J.J. Beitler,2,5 and M.K. Khan2; 1Department of Radiation Oncology at Emory University, Atlanta, GA, 2Department of Radiation Oncology, Winship Cancer Institute at Emory University, Atlanta, GA, 3Department of Biostatistics and Bioinformatics, Winship Cancer Institute at Emory