A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients

Gespeichert in:

Autor*in:	Chopra, N. [verfasserIn] Dou, T. [verfasserIn] Sharp, G. [verfasserIn] Sajo, E. [verfasserIn] Mak, R.H. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	Enthalten in: International journal of radiation oncology, biology, physics - Amsterdam [u.a.] : Elsevier Science, 1975, 108, Seite e777
Übergeordnetes Werk:	volume:108 ; pages:e777

DOI / URN:	10.1016/j.ijrobp.2020.07.231

Katalog-ID:	ELV004867106

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spelling	10.1016/j.ijrobp.2020.07.231 doi (DE-627)ELV004867106 (ELSEVIER)S0360-3016(20)31650-3 DE-627 ger DE-627 rda eng 610 DE-600 44.64 bkl 44.81 bkl Chopra, N. verfasserin aut A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dou, T. verfasserin aut Sharp, G. verfasserin aut Sajo, E. verfasserin aut Mak, R.H. verfasserin aut Enthalten in International journal of radiation oncology, biology, physics Amsterdam [u.a.] : Elsevier Science, 1975 108, Seite e777 Online-Ressource (DE-627)306659662 (DE-600)1500486-7 (DE-576)081986319 1879-355X nnns volume:108 pages:e777 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.64 Radiologie 44.81 Onkologie AR 108 e777
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allfieldsGer	10.1016/j.ijrobp.2020.07.231 doi (DE-627)ELV004867106 (ELSEVIER)S0360-3016(20)31650-3 DE-627 ger DE-627 rda eng 610 DE-600 44.64 bkl 44.81 bkl Chopra, N. verfasserin aut A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dou, T. verfasserin aut Sharp, G. verfasserin aut Sajo, E. verfasserin aut Mak, R.H. verfasserin aut Enthalten in International journal of radiation oncology, biology, physics Amsterdam [u.a.] : Elsevier Science, 1975 108, Seite e777 Online-Ressource (DE-627)306659662 (DE-600)1500486-7 (DE-576)081986319 1879-355X nnns volume:108 pages:e777 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.64 Radiologie 44.81 Onkologie AR 108 e777
allfieldsSound	10.1016/j.ijrobp.2020.07.231 doi (DE-627)ELV004867106 (ELSEVIER)S0360-3016(20)31650-3 DE-627 ger DE-627 rda eng 610 DE-600 44.64 bkl 44.81 bkl Chopra, N. verfasserin aut A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dou, T. verfasserin aut Sharp, G. verfasserin aut Sajo, E. verfasserin aut Mak, R.H. verfasserin aut Enthalten in International journal of radiation oncology, biology, physics Amsterdam [u.a.] : Elsevier Science, 1975 108, Seite e777 Online-Ressource (DE-627)306659662 (DE-600)1500486-7 (DE-576)081986319 1879-355X nnns volume:108 pages:e777 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.64 Radiologie 44.81 Onkologie AR 108 e777
language	English
source	Enthalten in International journal of radiation oncology, biology, physics 108, Seite e777 volume:108 pages:e777
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author	Chopra, N.
spellingShingle	Chopra, N. ddc 610 bkl 44.64 bkl 44.81 A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients
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title	A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients
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title_full	A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients
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title_sort	a combined radiomics-dosiomics machine learning approach improves prediction of radiation pneumonitis compared to dvh data in lung cancer patients
title_auth	A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients
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title_short	A Combined Radiomics-Dosiomics Machine Learning Approach Improves Prediction of Radiation Pneumonitis Compared to DVH Data in Lung Cancer Patients
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up_date	2024-07-07T00:24:41.156Z
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