Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis

Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyz...
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Autor*in:	Minfei Hu [verfasserIn] Fei Zheng [verfasserIn] Xiaohui Ma [verfasserIn] Linke Liu [verfasserIn] Chencong Shen [verfasserIn] Jianqiang Wu [verfasserIn] Chaoying Wang [verfasserIn] Li Yang [verfasserIn] Yiping Xu [verfasserIn] Lixia Zou [verfasserIn] Ling Fei [verfasserIn] Meiping Lu [verfasserIn] Xuefeng Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	juvenile dermatomyositis radiomics MRI diagnosis nomogram

Übergeordnetes Werk:	In: Journal of Clinical Medicine - MDPI AG, 2013, 11(2022), 22, p 6712
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:22, p 6712

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jcm11226712

Katalog-ID:	DOAJ083666753

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520			\|a Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM.
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allfields	10.3390/jcm11226712 doi (DE-627)DOAJ083666753 (DE-599)DOAJ6bd7005014f74443b663feb9b127c675 DE-627 ger DE-627 rakwb eng Minfei Hu verfasserin aut Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM. juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R Fei Zheng verfasserin aut Xiaohui Ma verfasserin aut Linke Liu verfasserin aut Chencong Shen verfasserin aut Jianqiang Wu verfasserin aut Chaoying Wang verfasserin aut Li Yang verfasserin aut Yiping Xu verfasserin aut Lixia Zou verfasserin aut Ling Fei verfasserin aut Meiping Lu verfasserin aut Xuefeng Xu verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 11(2022), 22, p 6712 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:11 year:2022 number:22, p 6712 https://doi.org/10.3390/jcm11226712 kostenfrei https://doaj.org/article/6bd7005014f74443b663feb9b127c675 kostenfrei https://www.mdpi.com/2077-0383/11/22/6712 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 6712
spelling	10.3390/jcm11226712 doi (DE-627)DOAJ083666753 (DE-599)DOAJ6bd7005014f74443b663feb9b127c675 DE-627 ger DE-627 rakwb eng Minfei Hu verfasserin aut Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM. juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R Fei Zheng verfasserin aut Xiaohui Ma verfasserin aut Linke Liu verfasserin aut Chencong Shen verfasserin aut Jianqiang Wu verfasserin aut Chaoying Wang verfasserin aut Li Yang verfasserin aut Yiping Xu verfasserin aut Lixia Zou verfasserin aut Ling Fei verfasserin aut Meiping Lu verfasserin aut Xuefeng Xu verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 11(2022), 22, p 6712 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:11 year:2022 number:22, p 6712 https://doi.org/10.3390/jcm11226712 kostenfrei https://doaj.org/article/6bd7005014f74443b663feb9b127c675 kostenfrei https://www.mdpi.com/2077-0383/11/22/6712 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 6712
allfields_unstemmed	10.3390/jcm11226712 doi (DE-627)DOAJ083666753 (DE-599)DOAJ6bd7005014f74443b663feb9b127c675 DE-627 ger DE-627 rakwb eng Minfei Hu verfasserin aut Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM. juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R Fei Zheng verfasserin aut Xiaohui Ma verfasserin aut Linke Liu verfasserin aut Chencong Shen verfasserin aut Jianqiang Wu verfasserin aut Chaoying Wang verfasserin aut Li Yang verfasserin aut Yiping Xu verfasserin aut Lixia Zou verfasserin aut Ling Fei verfasserin aut Meiping Lu verfasserin aut Xuefeng Xu verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 11(2022), 22, p 6712 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:11 year:2022 number:22, p 6712 https://doi.org/10.3390/jcm11226712 kostenfrei https://doaj.org/article/6bd7005014f74443b663feb9b127c675 kostenfrei https://www.mdpi.com/2077-0383/11/22/6712 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 6712
allfieldsGer	10.3390/jcm11226712 doi (DE-627)DOAJ083666753 (DE-599)DOAJ6bd7005014f74443b663feb9b127c675 DE-627 ger DE-627 rakwb eng Minfei Hu verfasserin aut Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM. juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R Fei Zheng verfasserin aut Xiaohui Ma verfasserin aut Linke Liu verfasserin aut Chencong Shen verfasserin aut Jianqiang Wu verfasserin aut Chaoying Wang verfasserin aut Li Yang verfasserin aut Yiping Xu verfasserin aut Lixia Zou verfasserin aut Ling Fei verfasserin aut Meiping Lu verfasserin aut Xuefeng Xu verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 11(2022), 22, p 6712 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:11 year:2022 number:22, p 6712 https://doi.org/10.3390/jcm11226712 kostenfrei https://doaj.org/article/6bd7005014f74443b663feb9b127c675 kostenfrei https://www.mdpi.com/2077-0383/11/22/6712 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 6712
allfieldsSound	10.3390/jcm11226712 doi (DE-627)DOAJ083666753 (DE-599)DOAJ6bd7005014f74443b663feb9b127c675 DE-627 ger DE-627 rakwb eng Minfei Hu verfasserin aut Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM. juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R Fei Zheng verfasserin aut Xiaohui Ma verfasserin aut Linke Liu verfasserin aut Chencong Shen verfasserin aut Jianqiang Wu verfasserin aut Chaoying Wang verfasserin aut Li Yang verfasserin aut Yiping Xu verfasserin aut Lixia Zou verfasserin aut Ling Fei verfasserin aut Meiping Lu verfasserin aut Xuefeng Xu verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 11(2022), 22, p 6712 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:11 year:2022 number:22, p 6712 https://doi.org/10.3390/jcm11226712 kostenfrei https://doaj.org/article/6bd7005014f74443b663feb9b127c675 kostenfrei https://www.mdpi.com/2077-0383/11/22/6712 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 22, p 6712
language	English
source	In Journal of Clinical Medicine 11(2022), 22, p 6712 volume:11 year:2022 number:22, p 6712
sourceStr	In Journal of Clinical Medicine 11(2022), 22, p 6712 volume:11 year:2022 number:22, p 6712
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	juvenile dermatomyositis radiomics MRI diagnosis nomogram Medicine R
isfreeaccess_bool	true
container_title	Journal of Clinical Medicine
authorswithroles_txt_mv	Minfei Hu @@aut@@ Fei Zheng @@aut@@ Xiaohui Ma @@aut@@ Linke Liu @@aut@@ Chencong Shen @@aut@@ Jianqiang Wu @@aut@@ Chaoying Wang @@aut@@ Li Yang @@aut@@ Yiping Xu @@aut@@ Lixia Zou @@aut@@ Ling Fei @@aut@@ Meiping Lu @@aut@@ Xuefeng Xu @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	718632478
id	DOAJ083666753
language_de	englisch
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author	Minfei Hu
spellingShingle	Minfei Hu misc juvenile dermatomyositis misc radiomics misc MRI misc diagnosis misc nomogram misc Medicine misc R Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis
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topic_title	Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis juvenile dermatomyositis radiomics MRI diagnosis nomogram
topic	misc juvenile dermatomyositis misc radiomics misc MRI misc diagnosis misc nomogram misc Medicine misc R
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title	Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis
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title_full	Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis
author_sort	Minfei Hu
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author_browse	Minfei Hu Fei Zheng Xiaohui Ma Linke Liu Chencong Shen Jianqiang Wu Chaoying Wang Li Yang Yiping Xu Lixia Zou Ling Fei Meiping Lu Xuefeng Xu
container_volume	11
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author-letter	Minfei Hu
doi_str_mv	10.3390/jcm11226712
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title_sort	assessment of thigh mri radiomics and clinical characteristics for assisting in discrimination of juvenile dermatomyositis
title_auth	Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis
abstract	Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM.
abstractGer	Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM.
abstract_unstemmed	Magnetic resonance imaging (MRI) is an important non-invasive examination in the early diagnosis of juvenile dermatomyositis (JDM). We aimed to evaluate the feasibility of radiomics to establish a quantitative analysis of MRI images. Radiomics and machine learning were used to retrospectively analyze MRI T2 fat suppression sequences and relevant clinical data. The model associated with radiomics features was established using a cohort of patients who underwent thigh MRI at the children’s hospital from June 2014 to September 2021. In total, 75 patients with JDM and 75 control children were included in the training cohort (<i<n</i< = 102) and validation cohort (<i<n</i< = 48). The independent factors including lower muscle strength (OR, 0.75; 95% CI, 0.59–0.90), higher creatine kinase (CK) level (OR, 1.65; 95% CI, 1.20–2.38), and higher radiomics score (OR, 2.30; 95% CI, 1.63–3.62) were associated with a clinical diagnosis of JDM. The combined model achieved good discrimination performance compared the radiomics score model under linear discriminant analyses in the training cohort (AUC, 0.949; 95% CI, 0.912–0.986 vs. AUC, 0.912; 95% CI, 0.858–0.967; <i<p</i< = 0.02) and in the validation cohort (AUC, 0.945; 95% CI, 0.878–1 vs. AUC, 0.905; 95% CI, 0.812–0.998; <i<p</i< = 0.03). The combined model showed the diagnostic value was not weaker than the biopsy (AUC, 0.950; 95% CI, 0.919–0.981, <i<n</i< = 150 vs. AUC, 0.952; 95% CI, 0.889–1, <i<n</i< = 72; <i<p</i< = 0.95) and electromyogram (EMG) (AUC, 0.950; 95% CI, 0.919–0.981 vs. AUC, 0.900; 95% CI, 0.852–0.948; <i<p</i< = 0.10) among all the patients. The combination of radiomics features extracted from the MRI and non-invasive clinical characteristics obtained a pronounced discriminative performance to assist in discriminating JDM.
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title_short	Assessment of Thigh MRI Radiomics and Clinical Characteristics for Assisting in Discrimination of Juvenile Dermatomyositis
url	https://doi.org/10.3390/jcm11226712 https://doaj.org/article/6bd7005014f74443b663feb9b127c675 https://www.mdpi.com/2077-0383/11/22/6712 https://doaj.org/toc/2077-0383
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