Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Abstract Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinica...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Asim Mazin, Samuel H. Hawkins, Olya Stringfield, Jasreman Dhillon, Brandon J. Manley, Daniel K. Jeong, Natarajan Raghunand
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2fe9e616bc764151986167520d42be11
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2fe9e616bc764151986167520d42be11
record_format	dspace
spelling	oai:doaj.org-article:2fe9e616bc764151986167520d42be112021-12-02T12:11:07ZIdentification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI10.1038/s41598-021-83271-42045-2322https://doaj.org/article/2fe9e616bc764151986167520d42be112021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83271-4https://doaj.org/toc/2045-2322Abstract Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.Asim MazinSamuel H. HawkinsOlya StringfieldJasreman DhillonBrandon J. ManleyDaniel K. JeongNatarajan RaghunandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Asim Mazin Samuel H. Hawkins Olya Stringfield Jasreman Dhillon Brandon J. Manley Daniel K. Jeong Natarajan Raghunand Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
description	Abstract Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.
format	article
author	Asim Mazin Samuel H. Hawkins Olya Stringfield Jasreman Dhillon Brandon J. Manley Daniel K. Jeong Natarajan Raghunand
author_facet	Asim Mazin Samuel H. Hawkins Olya Stringfield Jasreman Dhillon Brandon J. Manley Daniel K. Jeong Natarajan Raghunand
author_sort	Asim Mazin
title	Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
title_short	Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
title_full	Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
title_fullStr	Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
title_full_unstemmed	Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
title_sort	identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2fe9e616bc764151986167520d42be11
work_keys_str_mv	AT asimmazin identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT samuelhhawkins identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT olyastringfield identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT jasremandhillon identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT brandonjmanley identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT danielkjeong identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri AT natarajanraghunand identificationofsarcomatoiddifferentiationinrenalcellcarcinomabymachinelearningonmultiparametricmri
_version_	1718394619720368128

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Ejemplares similares