Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 μm MP2RAGE MRI at 7 T.


Journal

Investigative radiology
ISSN: 1536-0210
Titre abrégé: Invest Radiol
Pays: United States
ID NLM: 0045377

Informations de publication

Date de publication:
14 Jun 2024
Historique:
medline: 19 6 2024
pubmed: 19 6 2024
entrez: 19 6 2024
Statut: aheadofprint

Résumé

The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution. Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm3) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures. At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex (d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter (d = -0.81, P = 0.039), thalamus (d = 1.43, P < 0.001), globus pallidus (d = 0.92, P = 0.020), caudate nucleus (d = -0.83, P = 0.039), and corpus callosum (d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes (d = 1.05, P = 0.009). Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.

Identifiants

pubmed: 38896439
doi: 10.1097/RLI.0000000000001088
pii: 00004424-990000000-00223
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of interest and sources of funding: M.E.M. received honoraria from GE for lectures. For the remaining authors, no conflicts of interest were declared.

Références

Hagiwara A, Fujita S, Kurokawa R, et al. Multiparametric MRI: from simultaneous rapid acquisition methods and analysis techniques using scoring, machine learning, radiomics, and deep learning to the generation of novel metrics. Invest Radiol. 2023;58:548–560.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–577.
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med. 2020;61:488–495.
Fritz B, Yi PH, Kijowski R, et al. Radiomics and deep learning for disease detection in musculoskeletal radiology: an overview of novel MRI- and CT-based approaches. Invest Radiol. 2023;58:3–13.
Nenning K-H, Gesperger J, Furtner J, et al. Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma. Neurooncol Adv. 2023;5:vdad136.
Wang Y, Zhu GQ, Yang R, et al. Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis. J Transl Med. 2023;21:734.
Wennmann M, Ming W, Bauer F, et al. Prediction of bone marrow biopsy results from MRI in multiple myeloma patients using deep learning and radiomics. Invest Radiol. 2023;58:754–765.
Liu Y, Wang Y, Wang X, et al. MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study. Cancer Imaging. 2024;24:16.
Chen D, Zhang R, Huang X, et al. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. Biomark Res. 2024;12:14.
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–762.
Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet. 2020;396:565–582.
Unger JM, Vaidya R, Albain KS, et al. Sex differences in risk of severe adverse events in patients receiving immunotherapy, targeted therapy, or chemotherapy in cancer clinical trials. J Clin Oncol. 2022;40:1474–1486.
Ruigrok AN, Salimi-Khorshidi G, Lai MC, et al. A meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev. 2014;39:34–50.
Rijpkema M, Everaerd D, van der Pol C, et al. Normal sexual dimorphism in the human basal ganglia. Hum Brain Mapp. 2012;33:1246–1252.
Luders E, Toga AW, Thompson PM. Why size matters: differences in brain volume account for apparent sex differences in callosal anatomy: the sexual dimorphism of the corpus callosum. Neuroimage. 2014;84:820–824.
Mayerhoefer ME, Szomolanyi P, Jirak D, et al. Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. Med Phys. 2009;36:1236–1243.
Shafiq-Ul-Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050–1062.
Pardoe HR, Antony AR, Hetherington H, et al. High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI. Hum Brain Mapp. 2021;42:2089–2098.
Hetherington HP, Avdievich NI, Kuznetsov AM, et al. RF shimming for spectroscopic localization in the human brain at 7 T. Magn Reson Med. 2010;63:9–19.
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30:1323–1341.
Li W, Wang G, Fidon L, et al. On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. Information Processing in Medical Imaging: 25th International Conference, IPMI 2017. Lecture Notes in Computer Science. 2017;348–360. doi:10.1007/978-3-319-59050-9_28.
doi: 10.1007/978-3-319-59050-9_28
Cardoso MJ, Modat M, Wolz R, et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging. 2015;34:1976–1988.
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–e107.
Hoebel KV, Patel JB, Beers AL, et al. Radiomics repeatability pitfalls in a scan-rescan MRI study of glioblastoma. Radiol Artif Intell. 2020;3:e190199.
Carré A, Klausner G, Edjlali M, et al. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep. 2020;10:12340.
Sanvito F, Kaufmann TJ, Cloughesy TF, et al. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. Front Radiol. 2023;3:126761.
Jehi L, Yardi R, Chagin K, et al. Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: a retrospective analysis. Lancet Neurol. 2015;14:283–290.
Alifano M, Daffré E, Brouchet L, et al. Prognostic score and sex-specific nomograms to predict survival in resectable lung cancer: a French nationwide study from the Epithor cohort database. Lancet Reg Health Eur. 2022;26:100566.
Orlhac F, Lecler A, Savatovski J, et al. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol. 2021;31:2272–2280.
Leithner D, Nevin RB, Gibbs P, et al. ComBat harmonization for MRI radiomics: impact on nonbinary tissue classification by machine learning. Invest Radiol. 2023;58:697–701.
Richter S, Winzeck S, Correia MM, et al. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. Neuroimage Rep. 2022;2:None. doi:10.1016/j.ynirp.2022.100136.
doi: 10.1016/j.ynirp.2022.100136
Wichtmann BD, Harder FN, Weiss K, et al. Influence of image processing on radiomic features from magnetic resonance imaging. Invest Radiol. 2023;58:199–208.
Mayerhoefer ME, Szomolanyi P, Jirak D, et al. Effects of magnetic resonance image interpolation on the results of texture-based pattern classification: a phantom study. Invest Radiol. 2009;44:405–411.
Chaudhari AS, Fang Z, Kogan F, et al. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med. 2018;80:2139–2154.
Lin DJ, Walter SS, Fritz J. Artificial intelligence-driven ultra-fast superresolution MRI: 10-fold accelerated musculoskeletal turbo spin echo MRI within reach. Invest Radiol. 2023;58:28–42.
Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol. 2019;54:221–228.
Welch ML, McIntosh C, Haibe-Kains B, et al. Vulnerabilities of radiomic signature development: the need for safeguards. Radiother Oncol. 2019;130:2–9.

Auteurs

Marius E Mayerhoefer (ME)

From the Department of Radiology, NYU Grossman School of Medicine, New York, NY (M.E.M., T.M.S., D.L., S.W.); Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria (M.E.M., M.W.); Florey Institute of Neuroscience and Mental Health, Melbourne, Australia (H.R.P.); Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, NY (H.R.P.); and Department of Radiology, University of Missouri Columbia, Columbia, MO (J.W.P.).

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