Uncovering associations between data-driven learned qMRI biomarkers and chronic pain

Abstract Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alejandro G. Morales, Jinhee J. Lee, Francesco Caliva, Claudia Iriondo, Felix Liu, Sharmila Majumdar, Valentina Pedoia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1bfd63fb3ff1420cb11f0eccd2da2675
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1bfd63fb3ff1420cb11f0eccd2da2675
record_format	dspace
spelling	oai:doaj.org-article:1bfd63fb3ff1420cb11f0eccd2da26752021-11-14T12:18:23ZUncovering associations between data-driven learned qMRI biomarkers and chronic pain10.1038/s41598-021-01111-x2045-2322https://doaj.org/article/1bfd63fb3ff1420cb11f0eccd2da26752021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01111-xhttps://doaj.org/toc/2045-2322Abstract Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T2 relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic pain DL models are used to evaluate the locations of features associated with presence and absence of pain. For the cartilage thickness biomarker, the presence of features sensitive for pain presence were generally located in the medial side, while the features specific for pain absence were generally located in the anterior lateral side. This suggests that the association of cartilage thickness and pain varies, requiring a more personalized averaging strategy. We propose a novel DL-guided definition for cartilage thickness spatial averaging based on Grad-CAM weights. We showed a significant improvement modeling chronic knee pain with the inclusion of the novel biomarker definition: likelihood ratio test p-values of 7.01 × 10–33 and 1.93 × 10–14 for DL-guided cartilage thickness averaging for the femur and tibia, respectively, compared to the cartilage thickness compartment averaging.Alejandro G. MoralesJinhee J. LeeFrancesco CalivaClaudia IriondoFelix LiuSharmila MajumdarValentina PedoiaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alejandro G. Morales Jinhee J. Lee Francesco Caliva Claudia Iriondo Felix Liu Sharmila Majumdar Valentina Pedoia Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
description	Abstract Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T2 relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic pain DL models are used to evaluate the locations of features associated with presence and absence of pain. For the cartilage thickness biomarker, the presence of features sensitive for pain presence were generally located in the medial side, while the features specific for pain absence were generally located in the anterior lateral side. This suggests that the association of cartilage thickness and pain varies, requiring a more personalized averaging strategy. We propose a novel DL-guided definition for cartilage thickness spatial averaging based on Grad-CAM weights. We showed a significant improvement modeling chronic knee pain with the inclusion of the novel biomarker definition: likelihood ratio test p-values of 7.01 × 10–33 and 1.93 × 10–14 for DL-guided cartilage thickness averaging for the femur and tibia, respectively, compared to the cartilage thickness compartment averaging.
format	article
author	Alejandro G. Morales Jinhee J. Lee Francesco Caliva Claudia Iriondo Felix Liu Sharmila Majumdar Valentina Pedoia
author_facet	Alejandro G. Morales Jinhee J. Lee Francesco Caliva Claudia Iriondo Felix Liu Sharmila Majumdar Valentina Pedoia
author_sort	Alejandro G. Morales
title	Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
title_short	Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
title_full	Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
title_fullStr	Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
title_full_unstemmed	Uncovering associations between data-driven learned qMRI biomarkers and chronic pain
title_sort	uncovering associations between data-driven learned qmri biomarkers and chronic pain
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1bfd63fb3ff1420cb11f0eccd2da2675
work_keys_str_mv	AT alejandrogmorales uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT jinheejlee uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT francescocaliva uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT claudiairiondo uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT felixliu uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT sharmilamajumdar uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain AT valentinapedoia uncoveringassociationsbetweendatadrivenlearnedqmribiomarkersandchronicpain
_version_	1718429274070843392

Uncovering associations between data-driven learned qMRI biomarkers and chronic pain

Ejemplares similares