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Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

Author

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  • Tsair-Fwu Lee
  • Pei-Ju Chao
  • Hui-Min Ting
  • Liyun Chang
  • Yu-Jie Huang
  • Jia-Ming Wu
  • Hung-Yu Wang
  • Mong-Fong Horng
  • Chun-Ming Chang
  • Jen-Hong Lan
  • Ya-Yu Huang
  • Fu-Min Fang
  • Stephen Wan Leung

Abstract

Purpose: The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials: Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results: Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions: Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

Suggested Citation

  • Tsair-Fwu Lee & Pei-Ju Chao & Hui-Min Ting & Liyun Chang & Yu-Jie Huang & Jia-Ming Wu & Hung-Yu Wang & Mong-Fong Horng & Chun-Ming Chang & Jen-Hong Lan & Ya-Yu Huang & Fu-Min Fang & Stephen Wan Leung, 2014. "Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0089700
    DOI: 10.1371/journal.pone.0089700
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    References listed on IDEAS

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    1. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
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    2. Akram Farhadi & Joshua J. Chern & Daniel Hirsh & Tod Davis & Mingyoung Jo & Frederick Maier & Khaled Rasheed, 2018. "Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data," Forecasting, MDPI, vol. 1(1), pages 1-12, August.
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    4. Ekele Alih & Hong Choon Ong, 2015. "Cluster-based multivariate outlier identification and re-weighted regression in linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 938-955, May.
    5. Laura Cella & Giuseppe Palma & Joseph O Deasy & Jung Hun Oh & Raffaele Liuzzi & Vittoria D’Avino & Manuel Conson & Novella Pugliese & Marco Picardi & Marco Salvatore & Roberto Pacelli, 2014. "Complication Probability Models for Radiation-Induced Heart Valvular Dysfunction: Do Heart-Lung Interactions Play a Role?," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.

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