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.

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.

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http://www.wikidata.org/entity/Q35107930
DegreeCox - a network-based regularization method for survival analysisWeb search activity data accurately predict population chronic disease risk in the USA.The different dose-volume effects of normal tissue complication probability using LASSO for acute small-bowel toxicity during radiotherapy in gynecological patients with or without prior abdominal surgery.Complication probability models for radiation-induced heart valvular dysfunction: do heart-lung interactions play a role?Developing Multivariable Normal Tissue Complication Probability Model to Predict the Incidence of Symptomatic Radiation Pneumonitis among Breast Cancer PatientsPredictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.Patient- and therapy-related factors associated with the incidence of xerostomia in nasopharyngeal carcinoma patients receiving parotid-sparing helical tomotherapy.Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability ModelsPredicting radiation-induced valvular heart damage.Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers.Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma.Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods.A prognostic 4-gene expression signature for squamous cell lung carcinoma.Internal and external generalizability of temporal dose-response relationships for xerostomia following IMRT for head and neck cancer.Longitudinal drop-out and weighting against its bias.Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.
P2860

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.

Item
http://www.wikidata.org/entity/Q35107930
description
2014 nî lūn-bûn
@nan
2014 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Using multivariate regression ...... rapy for head and neck cancer.
@ast
Using multivariate regression ...... rapy for head and neck cancer.
@en
type
label
Using multivariate regression ...... rapy for head and neck cancer.
@ast
Using multivariate regression ...... rapy for head and neck cancer.
@en
prefLabel
Using multivariate regression ...... rapy for head and neck cancer.
@ast
Using multivariate regression ...... rapy for head and neck cancer.
@en
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Using multivariate regression ...... rapy for head and neck cancer.
@en
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P2860
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10.1371/JOURNAL.PONE.0089700
P407
P433
P478
P577
P5875
P698
P932