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MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

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MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes. / Bretzner, Martin; Bonkhoff, Anna K; Schirmer, Markus D; Hong, Sungmin; Dalca, Adrian V; Donahue, Kathleen L; Giese, Anne-Katrin; Etherton, Mark R; Rist, Pamela M; Nardin, Marco; Marinescu, Razvan; Wang, Clinton; Regenhardt, Robert W; Leclerc, Xavier; Lopes, Renaud; Benavente, Oscar R; Cole, John W; Donatti, Amanda; Griessenauer, Christoph J; Heitsch, Laura; Holmegaard, Lukas; Jood, Katarina; Jimenez-Conde, Jordi; Kittner, Steven J; Lemmens, Robin; Levi, Christopher R; McArdle, Patrick F; McDonough, Caitrin W; Meschia, James F; Phuah, Chia-Ling; Rolfs, Arndt; Ropele, Stefan; Rosand, Jonathan; Roquer, Jaume; Rundek, Tatjana; Sacco, Ralph L; Schmidt, Reinhold; Sharma, Pankaj; Slowik, Agnieszka; Sousa, Alessandro; Stanne, Tara M; Strbian, Daniel; Tatlisumak, Turgut; Thijs, Vincent; Vagal, Achala; Wasselius, Johan; Woo, Daniel; Wu, Ona; Zand, Ramin; Worrall, Bradford B; Maguire, Jane M; Lindgren, Arne; Jern, Christina; Golland, Polina; Kuchcinski, Grégory; Rost, Natalia S.

In: FRONT NEUROSCI-SWITZ, Vol. 15, 691244, 2021.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Bretzner, M, Bonkhoff, AK, Schirmer, MD, Hong, S, Dalca, AV, Donahue, KL, Giese, A-K, Etherton, MR, Rist, PM, Nardin, M, Marinescu, R, Wang, C, Regenhardt, RW, Leclerc, X, Lopes, R, Benavente, OR, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McArdle, PF, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Wu, O, Zand, R, Worrall, BB, Maguire, JM, Lindgren, A, Jern, C, Golland, P, Kuchcinski, G & Rost, NS 2021, 'MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes', FRONT NEUROSCI-SWITZ, vol. 15, 691244. https://doi.org/10.3389/fnins.2021.691244

APA

Bretzner, M., Bonkhoff, A. K., Schirmer, M. D., Hong, S., Dalca, A. V., Donahue, K. L., Giese, A-K., Etherton, M. R., Rist, P. M., Nardin, M., Marinescu, R., Wang, C., Regenhardt, R. W., Leclerc, X., Lopes, R., Benavente, O. R., Cole, J. W., Donatti, A., Griessenauer, C. J., ... Rost, N. S. (2021). MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes. FRONT NEUROSCI-SWITZ, 15, [691244]. https://doi.org/10.3389/fnins.2021.691244

Vancouver

Bretzner M, Bonkhoff AK, Schirmer MD, Hong S, Dalca AV, Donahue KL et al. MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes. FRONT NEUROSCI-SWITZ. 2021;15. 691244. https://doi.org/10.3389/fnins.2021.691244

Bibtex

@article{3e3138f655924e6ca1dadb6fac08cd78,
title = "MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes",
abstract = "Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).Results: Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.",
author = "Martin Bretzner and Bonkhoff, {Anna K} and Schirmer, {Markus D} and Sungmin Hong and Dalca, {Adrian V} and Donahue, {Kathleen L} and Anne-Katrin Giese and Etherton, {Mark R} and Rist, {Pamela M} and Marco Nardin and Razvan Marinescu and Clinton Wang and Regenhardt, {Robert W} and Xavier Leclerc and Renaud Lopes and Benavente, {Oscar R} and Cole, {John W} and Amanda Donatti and Griessenauer, {Christoph J} and Laura Heitsch and Lukas Holmegaard and Katarina Jood and Jordi Jimenez-Conde and Kittner, {Steven J} and Robin Lemmens and Levi, {Christopher R} and McArdle, {Patrick F} and McDonough, {Caitrin W} and Meschia, {James F} and Chia-Ling Phuah and Arndt Rolfs and Stefan Ropele and Jonathan Rosand and Jaume Roquer and Tatjana Rundek and Sacco, {Ralph L} and Reinhold Schmidt and Pankaj Sharma and Agnieszka Slowik and Alessandro Sousa and Stanne, {Tara M} and Daniel Strbian and Turgut Tatlisumak and Vincent Thijs and Achala Vagal and Johan Wasselius and Daniel Woo and Ona Wu and Ramin Zand and Worrall, {Bradford B} and Maguire, {Jane M} and Arne Lindgren and Christina Jern and Polina Golland and Gr{\'e}gory Kuchcinski and Rost, {Natalia S}",
note = "Copyright {\textcopyright} 2021 Bretzner, Bonkhoff, Schirmer, Hong, Dalca, Donahue, Giese, Etherton, Rist, Nardin, Marinescu, Wang, Regenhardt, Leclerc, Lopes, Benavente, Cole, Donatti, Griessenauer, Heitsch, Holmegaard, Jood, Jimenez-Conde, Kittner, Lemmens, Levi, McArdle, McDonough, Meschia, Phuah, Rolfs, Ropele, Rosand, Roquer, Rundek, Sacco, Schmidt, Sharma, Slowik, Sousa, Stanne, Strbian, Tatlisumak, Thijs, Vagal, Wasselius, Woo, Wu, Zand, Worrall, Maguire, Lindgren, Jern, Golland, Kuchcinski and Rost.",
year = "2021",
doi = "10.3389/fnins.2021.691244",
language = "English",
volume = "15",
journal = "FRONT NEUROSCI-SWITZ",
issn = "1662-453X",
publisher = "Frontiers Media S. A.",

}

RIS

TY - JOUR

T1 - MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

AU - Bretzner, Martin

AU - Bonkhoff, Anna K

AU - Schirmer, Markus D

AU - Hong, Sungmin

AU - Dalca, Adrian V

AU - Donahue, Kathleen L

AU - Giese, Anne-Katrin

AU - Etherton, Mark R

AU - Rist, Pamela M

AU - Nardin, Marco

AU - Marinescu, Razvan

AU - Wang, Clinton

AU - Regenhardt, Robert W

AU - Leclerc, Xavier

AU - Lopes, Renaud

AU - Benavente, Oscar R

AU - Cole, John W

AU - Donatti, Amanda

AU - Griessenauer, Christoph J

AU - Heitsch, Laura

AU - Holmegaard, Lukas

AU - Jood, Katarina

AU - Jimenez-Conde, Jordi

AU - Kittner, Steven J

AU - Lemmens, Robin

AU - Levi, Christopher R

AU - McArdle, Patrick F

AU - McDonough, Caitrin W

AU - Meschia, James F

AU - Phuah, Chia-Ling

AU - Rolfs, Arndt

AU - Ropele, Stefan

AU - Rosand, Jonathan

AU - Roquer, Jaume

AU - Rundek, Tatjana

AU - Sacco, Ralph L

AU - Schmidt, Reinhold

AU - Sharma, Pankaj

AU - Slowik, Agnieszka

AU - Sousa, Alessandro

AU - Stanne, Tara M

AU - Strbian, Daniel

AU - Tatlisumak, Turgut

AU - Thijs, Vincent

AU - Vagal, Achala

AU - Wasselius, Johan

AU - Woo, Daniel

AU - Wu, Ona

AU - Zand, Ramin

AU - Worrall, Bradford B

AU - Maguire, Jane M

AU - Lindgren, Arne

AU - Jern, Christina

AU - Golland, Polina

AU - Kuchcinski, Grégory

AU - Rost, Natalia S

N1 - Copyright © 2021 Bretzner, Bonkhoff, Schirmer, Hong, Dalca, Donahue, Giese, Etherton, Rist, Nardin, Marinescu, Wang, Regenhardt, Leclerc, Lopes, Benavente, Cole, Donatti, Griessenauer, Heitsch, Holmegaard, Jood, Jimenez-Conde, Kittner, Lemmens, Levi, McArdle, McDonough, Meschia, Phuah, Rolfs, Ropele, Rosand, Roquer, Rundek, Sacco, Schmidt, Sharma, Slowik, Sousa, Stanne, Strbian, Tatlisumak, Thijs, Vagal, Wasselius, Woo, Wu, Zand, Worrall, Maguire, Lindgren, Jern, Golland, Kuchcinski and Rost.

PY - 2021

Y1 - 2021

N2 - Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).Results: Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.

AB - Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).Results: Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.

U2 - 10.3389/fnins.2021.691244

DO - 10.3389/fnins.2021.691244

M3 - SCORING: Journal article

C2 - 34321995

VL - 15

JO - FRONT NEUROSCI-SWITZ

JF - FRONT NEUROSCI-SWITZ

SN - 1662-453X

M1 - 691244

ER -