Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed
Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type an...
Ausführliche Beschreibung
Autor*in: |
Chhabra, Avneesh [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 |
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Übergeordnetes Werk: |
Enthalten in: Skeletal radiology - Berlin : Springer, 1976, 51(2022), 10 vom: 27. Apr., Seite 1909-1922 |
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Übergeordnetes Werk: |
volume:51 ; year:2022 ; number:10 ; day:27 ; month:04 ; pages:1909-1922 |
Links: |
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DOI / URN: |
10.1007/s00256-022-04061-1 |
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Katalog-ID: |
SPR047857382 |
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520 | |a Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. | ||
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10.1007/s00256-022-04061-1 doi (DE-627)SPR047857382 (SPR)s00256-022-04061-1-e DE-627 ger DE-627 rakwb eng Chhabra, Avneesh verfasserin aut Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 Deshmukh, Swati D. aut Lutz, Amelie M. aut Fritz, Jan aut Sneag, Darryl B. aut Mogharrabi, Bayan aut Guirguis, Mina aut Andreisek, Gustav aut Xi, Yin aut Ahlawat, Shivani aut Enthalten in Skeletal radiology Berlin : Springer, 1976 51(2022), 10 vom: 27. Apr., Seite 1909-1922 (DE-627)254236855 (DE-600)1461957-X 1432-2161 nnns volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 https://dx.doi.org/10.1007/s00256-022-04061-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 51 2022 10 27 04 1909-1922 |
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10.1007/s00256-022-04061-1 doi (DE-627)SPR047857382 (SPR)s00256-022-04061-1-e DE-627 ger DE-627 rakwb eng Chhabra, Avneesh verfasserin aut Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 Deshmukh, Swati D. aut Lutz, Amelie M. aut Fritz, Jan aut Sneag, Darryl B. aut Mogharrabi, Bayan aut Guirguis, Mina aut Andreisek, Gustav aut Xi, Yin aut Ahlawat, Shivani aut Enthalten in Skeletal radiology Berlin : Springer, 1976 51(2022), 10 vom: 27. Apr., Seite 1909-1922 (DE-627)254236855 (DE-600)1461957-X 1432-2161 nnns volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 https://dx.doi.org/10.1007/s00256-022-04061-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 51 2022 10 27 04 1909-1922 |
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10.1007/s00256-022-04061-1 doi (DE-627)SPR047857382 (SPR)s00256-022-04061-1-e DE-627 ger DE-627 rakwb eng Chhabra, Avneesh verfasserin aut Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 Deshmukh, Swati D. aut Lutz, Amelie M. aut Fritz, Jan aut Sneag, Darryl B. aut Mogharrabi, Bayan aut Guirguis, Mina aut Andreisek, Gustav aut Xi, Yin aut Ahlawat, Shivani aut Enthalten in Skeletal radiology Berlin : Springer, 1976 51(2022), 10 vom: 27. Apr., Seite 1909-1922 (DE-627)254236855 (DE-600)1461957-X 1432-2161 nnns volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 https://dx.doi.org/10.1007/s00256-022-04061-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 51 2022 10 27 04 1909-1922 |
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10.1007/s00256-022-04061-1 doi (DE-627)SPR047857382 (SPR)s00256-022-04061-1-e DE-627 ger DE-627 rakwb eng Chhabra, Avneesh verfasserin aut Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 Deshmukh, Swati D. aut Lutz, Amelie M. aut Fritz, Jan aut Sneag, Darryl B. aut Mogharrabi, Bayan aut Guirguis, Mina aut Andreisek, Gustav aut Xi, Yin aut Ahlawat, Shivani aut Enthalten in Skeletal radiology Berlin : Springer, 1976 51(2022), 10 vom: 27. Apr., Seite 1909-1922 (DE-627)254236855 (DE-600)1461957-X 1432-2161 nnns volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 https://dx.doi.org/10.1007/s00256-022-04061-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 51 2022 10 27 04 1909-1922 |
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10.1007/s00256-022-04061-1 doi (DE-627)SPR047857382 (SPR)s00256-022-04061-1-e DE-627 ger DE-627 rakwb eng Chhabra, Avneesh verfasserin aut Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 Deshmukh, Swati D. aut Lutz, Amelie M. aut Fritz, Jan aut Sneag, Darryl B. aut Mogharrabi, Bayan aut Guirguis, Mina aut Andreisek, Gustav aut Xi, Yin aut Ahlawat, Shivani aut Enthalten in Skeletal radiology Berlin : Springer, 1976 51(2022), 10 vom: 27. Apr., Seite 1909-1922 (DE-627)254236855 (DE-600)1461957-X 1432-2161 nnns volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 https://dx.doi.org/10.1007/s00256-022-04061-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 51 2022 10 27 04 1909-1922 |
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Enthalten in Skeletal radiology 51(2022), 10 vom: 27. Apr., Seite 1909-1922 volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 |
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Enthalten in Skeletal radiology 51(2022), 10 vom: 27. Apr., Seite 1909-1922 volume:51 year:2022 number:10 day:27 month:04 pages:1909-1922 |
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Peripheral nerve Neuropathy MRI MR neurography MRN NS-RADS |
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Chhabra, Avneesh @@aut@@ Deshmukh, Swati D. @@aut@@ Lutz, Amelie M. @@aut@@ Fritz, Jan @@aut@@ Sneag, Darryl B. @@aut@@ Mogharrabi, Bayan @@aut@@ Guirguis, Mina @@aut@@ Andreisek, Gustav @@aut@@ Xi, Yin @@aut@@ Ahlawat, Shivani @@aut@@ |
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Chhabra, Avneesh |
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Chhabra, Avneesh misc Peripheral nerve misc Neuropathy misc MRI misc MR neurography misc MRN misc NS-RADS Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed |
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Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed Peripheral nerve (dpeaa)DE-He213 Neuropathy (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR neurography (dpeaa)DE-He213 MRN (dpeaa)DE-He213 NS-RADS (dpeaa)DE-He213 |
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Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed |
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Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed |
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Chhabra, Avneesh Deshmukh, Swati D. Lutz, Amelie M. Fritz, Jan Sneag, Darryl B. Mogharrabi, Bayan Guirguis, Mina Andreisek, Gustav Xi, Yin Ahlawat, Shivani |
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neuropathy score reporting and data system (ns-rads): mri reporting guideline of peripheral neuropathy explained and reviewed |
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Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed |
abstract |
Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 |
abstractGer |
Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 |
abstract_unstemmed |
Abstract A standardized guideline and scoring system should be used for the MR imaging diagnosis of peripheral neuropathy. The MR imaging-based Neuropathy Score Reporting and Data System (NS-RADS) is a newly devised classification system (in press in AJR) that can be used to communicate both type and severity of peripheral neuropathy in the light of clinical history and examination findings. The spectrum of neuropathic conditions and peripheral nerve disorders covered in this system includes nerve injury, entrapment, neoplasm, diffuse neuropathy, and post-interventional states. This classification system also describes the temporal MR imaging appearances of regional muscle denervation changes. This review article is based on the multicenter validation study pre-published in American journal of Roentgenology and discusses technical considerations of optimal MR imaging for peripheral nerve evaluation and discusses the NS-RADS classification and its severity scales with illustration of conditions that fall under each classification. The readers can gain knowledge of the NS-RADS classification system and learn to apply it in their practices for improved inter-disciplinary communications and timely patient management. © The Author(s), under exclusive licence to International Skeletal Society (ISS) 2022 |
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container_issue |
10 |
title_short |
Neuropathy Score Reporting and Data System (NS-RADS): MRI Reporting Guideline of Peripheral Neuropathy Explained and Reviewed |
url |
https://dx.doi.org/10.1007/s00256-022-04061-1 |
remote_bool |
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author2 |
Deshmukh, Swati D. Lutz, Amelie M. Fritz, Jan Sneag, Darryl B. Mogharrabi, Bayan Guirguis, Mina Andreisek, Gustav Xi, Yin Ahlawat, Shivani |
author2Str |
Deshmukh, Swati D. Lutz, Amelie M. Fritz, Jan Sneag, Darryl B. Mogharrabi, Bayan Guirguis, Mina Andreisek, Gustav Xi, Yin Ahlawat, Shivani |
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doi_str |
10.1007/s00256-022-04061-1 |
up_date |
2024-07-03T15:26:53.459Z |
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score |
7.4008417 |