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-Reference-Cited by-同舟云学术

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

Published:2014-02-28 Issue:2 Volume:9 Page:e89700
ISSN:1932-6203
Container-title:PLoS ONE
language:en
Short-container-title:PLoS ONE

Author:

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

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference42 articles.

1. Correlation of idiopathic scoliosis assessments between newly developed milwaukee topographic scanner and Quantec;C Lim;Stud health technol inform,2012

2. Treatment planning constraints to avoid xerostomia in head-and-neck radiotherapy: an independent test of QUANTEC criteria using a prospectively collected dataset;V Moiseenko;Int J Radiat Oncol Biol Phys,2012

3. Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva: The role of dosimetric and clinical factors;I Beetz;Radiother Oncol,2012

4. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: The role of dosimetric and clinical factors;I Beetz;Radiother Oncol,2012

5. External validation of three dimensional conformal radiotherapy based NTCP models for patient-rated xerostomia and sticky saliva among patients treated with intensity modulated radiotherapy;I Beetz;Radiother Oncol,2012

Cited by 83 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting disease recurrence in patients with endometriosis: an observational study;BMC Medicine;2024-08-07

2. Predict nutrition-related adverse outcomes in head and neck cancer patients undergoing radiotherapy: A systematic review;Radiotherapy and Oncology;2024-08

3. NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy;International Journal of Radiation Oncology*Biology*Physics;2024-07

4. Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy;Radiation Protection Dosimetry;2024-06-26

5. Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning;Radiation Oncology;2024-06-24