Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas

The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yutao Wang, Qian Shao, Shuying Luo, Randi Fu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	high grade gliomas radiomics transfer learning magnetic resonance imaging nomogram Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c7810ae845ff44afa3a0184320e5f28a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c7810ae845ff44afa3a0184320e5f28a
record_format	dspace
spelling	oai:doaj.org-article:c7810ae845ff44afa3a0184320e5f28a2021-11-24T00:53:28ZDevelopment of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas10.3934/mbe.20214011551-0018https://doaj.org/article/c7810ae845ff44afa3a0184320e5f28a2021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021401?viewType=HTMLhttps://doaj.org/toc/1551-0018The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation cohort of 42 HGG patients were enrolled in this study. From each patient's 3D MRI, 1284 radiomics features were extracted, and 8192 deep features were extracted via transfer learning. By using Least Absolute Shrinkage and Selection Operator (LASSO) regression to select features, the radiomics signatures and deep signatures were generated. The radiomics and deep features were then analyzed synthetically to generate a combined signature. Finally, the nomogram was developed by integrating the combined signature and clinical predictors. The radiomics and deep signatures were significantly associated with HGG patients' survival time. The signature derived from the synthesized radiomics and deep features showed a better prognostic performance than those from radiomics or deep features alone. The nomogram we developed takes the advantages of both radiomics and deep signatures, and also integrates the predictive ability of clinical indicators. The calibration curve shows our predicted survival time by the nomogram was very close to the actual time.Yutao WangQian Shao Shuying LuoRandi FuAIMS Pressarticlehigh grade gliomasradiomicstransfer learningmagnetic resonance imagingnomogramBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8084-8095 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high grade gliomas radiomics transfer learning magnetic resonance imaging nomogram Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	high grade gliomas radiomics transfer learning magnetic resonance imaging nomogram Biotechnology TP248.13-248.65 Mathematics QA1-939 Yutao Wang Qian Shao Shuying Luo Randi Fu Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
description	The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation cohort of 42 HGG patients were enrolled in this study. From each patient's 3D MRI, 1284 radiomics features were extracted, and 8192 deep features were extracted via transfer learning. By using Least Absolute Shrinkage and Selection Operator (LASSO) regression to select features, the radiomics signatures and deep signatures were generated. The radiomics and deep features were then analyzed synthetically to generate a combined signature. Finally, the nomogram was developed by integrating the combined signature and clinical predictors. The radiomics and deep signatures were significantly associated with HGG patients' survival time. The signature derived from the synthesized radiomics and deep features showed a better prognostic performance than those from radiomics or deep features alone. The nomogram we developed takes the advantages of both radiomics and deep signatures, and also integrates the predictive ability of clinical indicators. The calibration curve shows our predicted survival time by the nomogram was very close to the actual time.
format	article
author	Yutao Wang Qian Shao Shuying Luo Randi Fu
author_facet	Yutao Wang Qian Shao Shuying Luo Randi Fu
author_sort	Yutao Wang
title	Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
title_short	Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
title_full	Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
title_fullStr	Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
title_full_unstemmed	Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas
title_sort	development of a nomograph integrating radiomics and deep features based on mri to predict the prognosis of high grade gliomas
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/c7810ae845ff44afa3a0184320e5f28a
work_keys_str_mv	AT yutaowang developmentofanomographintegratingradiomicsanddeepfeaturesbasedonmritopredicttheprognosisofhighgradegliomas AT qianshao developmentofanomographintegratingradiomicsanddeepfeaturesbasedonmritopredicttheprognosisofhighgradegliomas AT shuyingluo developmentofanomographintegratingradiomicsanddeepfeaturesbasedonmritopredicttheprognosisofhighgradegliomas AT randifu developmentofanomographintegratingradiomicsanddeepfeaturesbasedonmritopredicttheprognosisofhighgradegliomas
_version_	1718416058835009536

Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas

Ejemplares similares