Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease
Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It wou...
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| Published in | Journal of translational medicine Vol. 22; no. 1; pp. 56 - 14 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
13.01.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1479-5876 1479-5876 |
| DOI | 10.1186/s12967-023-04792-2 |
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| Abstract | Background
Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.
Methods
In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.
Results
The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.
Conclusions
The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. |
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| AbstractList | Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.BACKGROUNDRadiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.METHODSIn this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.RESULTSThe support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.CONCLUSIONSThe WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. Methods In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. Results The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. Conclusions The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. In this retrospective study, we enrolled 127 patients with TED that received 4*5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0*961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0*766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0*760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0*916, LR) led to different conclusions. The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. Methods In this retrospective study, we enrolled 127 patients with TED that received 4*5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. Results The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0*961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0*766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0*760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0*916, LR) led to different conclusions. Conclusions The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. Keywords: Thyroid eye disease, MRI, Radiomics analysis, Intravenous glucocorticoid, Response prediction, Multi-organ segmentation Abstract Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. Methods In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. Results The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. Conclusions The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. BackgroundRadiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.MethodsIn this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.ResultsThe support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.ConclusionsThe WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result. |
| ArticleNumber | 56 |
| Audience | Academic |
| Author | Tao, Xiaofeng Xia, Duojin Jiang, Mengda Fan, Xianqun Zhu, Ling Song, Xuefei Zhang, Haiyang Zhou, Huifang Chan, Hoi Chi Zhou, Lei Liu, Yuting Li, Yinwei Sun, Jing Xu, Jiashuo Zhang, Huijie |
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| CitedBy_id | crossref_primary_10_1007_s10792_024_03138_1 crossref_primary_10_1016_j_preteyeres_2025_101350 crossref_primary_10_3389_fendo_2024_1356055 crossref_primary_10_1007_s42979_024_03451_7 |
| Cites_doi | 10.1002/jmri.28088 10.1097/WNO.0000000000000128 10.1007/s12020-022-03167-9 10.1002/jmri.28498 10.1530/EJE-21-0479 10.1002/jmri.29114 10.1007/s00330-021-08300-2 10.1155/2017/3196059 10.1007/s11548-020-02281-1 10.1016/j.eclinm.2021.101215 10.3389/fendo.2022.1001349 10.4158/EP-2019-0133 10.1097/RLI.0000000000000722 10.1530/EJE-13-0611 10.1016/S2213-8587(16)30046-8 10.3892/etm.2016.3389 10.1109/JBHI.2022.3181791 10.1210/jc.2012-2389 10.1016/j.tem.2017.10.010 10.1167/iovs.08-2020 10.1148/radiology.172.3.2772184 10.1007/s12020-020-02367-5 10.3389/fendo.2021.614536 10.1038/s41574-019-0305-4 10.1016/j.bspc.2021.103373 10.1167/iovs.62.9.24 10.1145/2939672.2939785 10.1007/BF00994018 10.1056/NEJMra0905750 10.1089/105072502753600179 10.1016/j.eclinm.2021.101201 10.2967/jnumed.118.222893 10.3389/fendo.2022.895186 |
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| Keywords | Radiomics analysis Thyroid eye disease MRI Multi-organ segmentation Response prediction Intravenous glucocorticoid |
| Language | English |
| License | 2023. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. cc-by |
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| References | M Jiang (4792_CR19) 2022; 78 Oculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association (4792_CR4) 2022; 58 H Jiang (4792_CR9) 2018; 27 L Zhang (4792_CR25) 2022; 58 J Huang (4792_CR20) 2022; 13 MH Chen (4792_CR29) 2008; 49 M Zou (4792_CR33) 2021; 32 P Zhu (4792_CR21) 2022; 13 X Li (4792_CR17) 2021; 43 G Vannucchi (4792_CR6) 2014; 170 N Hosten (4792_CR13) 1989; 172 DW Kim (4792_CR30) 2021; 62 ME Mayerhoefer (4792_CR15) 2020; 61 H Hu (4792_CR24) 2020; 70 M Zhou (4792_CR10) 2019; 25 C Cortes (4792_CR36) 1995; 20 RS Bahn (4792_CR2) 2010; 362 L Xu (4792_CR22) 2017; 2017 M Meng (4792_CR34) 2022; 26 4792_CR35 N Yokoyama (4792_CR11) 2002; 12 4792_CR12 P Liu (4792_CR27) 2021; 12 J Shi (4792_CR26) 2022; 72 H Hu (4792_CR32) 2016; 12 X Song (4792_CR23) 2020; 16 MT Bhatti (4792_CR28) 2014; 34 L Bartalena (4792_CR1) 2021; 185 H Hu (4792_CR14) 2022; 56 Y Hei (4792_CR31) 2008; 44 S Fu (4792_CR16) 2021; 42 J Vandewalle (4792_CR5) 2018; 29 L Bartalena (4792_CR7) 2012; 97 PN Taylor (4792_CR3) 2020; 16 WM Wiersinga (4792_CR8) 2016; 5 L Duron (4792_CR18) 2021; 56 |
| References_xml | – volume: 56 start-page: 862 issue: 3 year: 2022 ident: 4792_CR14 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.28088 – volume: 34 start-page: 186 issue: 2 year: 2014 ident: 4792_CR28 publication-title: J Neuroophthalmol doi: 10.1097/WNO.0000000000000128 – volume: 78 start-page: 321 issue: 2 year: 2022 ident: 4792_CR19 publication-title: Endocrine doi: 10.1007/s12020-022-03167-9 – volume: 58 start-page: 258 issue: 1 year: 2022 ident: 4792_CR25 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.28498 – volume: 185 start-page: G43 issue: 4 year: 2021 ident: 4792_CR1 publication-title: Eur J Endocrinol doi: 10.1530/EJE-21-0479 – ident: 4792_CR12 doi: 10.1002/jmri.29114 – volume: 32 start-page: 1931 issue: 3 year: 2021 ident: 4792_CR33 publication-title: Eur Radiol doi: 10.1007/s00330-021-08300-2 – volume: 2017 start-page: 3196059 year: 2017 ident: 4792_CR22 publication-title: Int J Endocrinol doi: 10.1155/2017/3196059 – volume: 16 start-page: 323 issue: 2 year: 2020 ident: 4792_CR23 publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-020-02281-1 – volume: 43 year: 2021 ident: 4792_CR17 publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2021.101215 – volume: 13 start-page: 1001349 year: 2022 ident: 4792_CR20 publication-title: Front Endocrinol doi: 10.3389/fendo.2022.1001349 – volume: 25 start-page: 1268 issue: 12 year: 2019 ident: 4792_CR10 publication-title: Endocr Pract doi: 10.4158/EP-2019-0133 – volume: 56 start-page: 173 issue: 3 year: 2021 ident: 4792_CR18 publication-title: Invest Radiol doi: 10.1097/RLI.0000000000000722 – volume: 170 start-page: 55 issue: 1 year: 2014 ident: 4792_CR6 publication-title: Eur J Endocrinol doi: 10.1530/EJE-13-0611 – volume: 5 start-page: 134 issue: 2 year: 2016 ident: 4792_CR8 publication-title: Lancet Diabetes Endocrinol doi: 10.1016/S2213-8587(16)30046-8 – volume: 12 start-page: 725 issue: 2 year: 2016 ident: 4792_CR32 publication-title: Exp Ther Med doi: 10.3892/etm.2016.3389 – volume: 26 start-page: 4497 issue: 9 year: 2022 ident: 4792_CR34 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2022.3181791 – volume: 97 start-page: 4454 issue: 12 year: 2012 ident: 4792_CR7 publication-title: J Clin Endocrinol Metab doi: 10.1210/jc.2012-2389 – volume: 29 start-page: 42 issue: 1 year: 2018 ident: 4792_CR5 publication-title: Trends Endocrinol Metab doi: 10.1016/j.tem.2017.10.010 – volume: 49 start-page: 4760 issue: 11 year: 2008 ident: 4792_CR29 publication-title: Invest Ophthalmol Vis Sci doi: 10.1167/iovs.08-2020 – volume: 172 start-page: 759 issue: 3 year: 1989 ident: 4792_CR13 publication-title: Radiology doi: 10.1148/radiology.172.3.2772184 – volume: 70 start-page: 372 issue: 2 year: 2020 ident: 4792_CR24 publication-title: Endocrine doi: 10.1007/s12020-020-02367-5 – volume: 12 start-page: 614536 year: 2021 ident: 4792_CR27 publication-title: Front Endocrinol doi: 10.3389/fendo.2021.614536 – volume: 16 start-page: 104 issue: 2 year: 2020 ident: 4792_CR3 publication-title: Nat Rev Endocrinol doi: 10.1038/s41574-019-0305-4 – volume: 72 year: 2022 ident: 4792_CR26 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.103373 – volume: 44 start-page: 423 issue: 5 year: 2008 ident: 4792_CR31 publication-title: Zhonghua Yan Ke Za Zhi – volume: 62 start-page: 24 issue: 9 year: 2021 ident: 4792_CR30 publication-title: Invest Ophth Vis Sci doi: 10.1167/iovs.62.9.24 – ident: 4792_CR35 doi: 10.1145/2939672.2939785 – volume: 58 start-page: 646 issue: 9 year: 2022 ident: 4792_CR4 publication-title: Trends Endocrinol Metab – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 4792_CR36 publication-title: Mach Learn doi: 10.1007/BF00994018 – volume: 362 start-page: 726 issue: 8 year: 2010 ident: 4792_CR2 publication-title: N Engl J Med doi: 10.1056/NEJMra0905750 – volume: 12 start-page: 223 issue: 3 year: 2002 ident: 4792_CR11 publication-title: Thyroid doi: 10.1089/105072502753600179 – volume: 42 year: 2021 ident: 4792_CR16 publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2021.101201 – volume: 61 start-page: 488 issue: 4 year: 2020 ident: 4792_CR15 publication-title: J Nucl Med doi: 10.2967/jnumed.118.222893 – volume: 13 year: 2022 ident: 4792_CR21 publication-title: Front Endocrinol doi: 10.3389/fendo.2022.895186 – volume: 27 start-page: 339 issue: 5 year: 2018 ident: 4792_CR9 publication-title: Ophthalmol China |
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| Snippet | Background
Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction... Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid... Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction... BackgroundRadiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction... Abstract Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response... |
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| SubjectTerms | Algorithms Biomedical and Life Sciences Biomedicine Care and treatment Corticosteroids Diagnosis Diplopia Dosage and administration Edema Eye diseases Glucocorticoids Glucocorticoids - therapeutic use Graves Ophthalmopathy - diagnostic imaging Health aspects Humans Image processing Intravenous administration Intravenous glucocorticoid Lacrimal gland and Nasolacrimal duct Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Medicine/Public Health Methods MRI Multi-organ segmentation Muscles Oculomotor system Optic nerve Orbit - diagnostic imaging Pathogenesis Prediction models Radiomics Radiomics analysis Response prediction Retrospective Studies Segmentation Soft tissues Thyroid Thyroid eye disease Thyroid gland Translational Ophthalmology Visual acuity |
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| Title | Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease |
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