Diffusion-Weighted Imaging (DWI) derived from PET/MRI for lymph node assessment in patients with Head and Neck Squamous Cell Carcinoma (HNSCC)
To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Retrospective study of 90 lymph nodes from 90 sub...
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| Published in | Cancer imaging Vol. 20; no. 1; pp. 56 - 12 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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BioMed Central Ltd
08.08.2020
BioMed Central BMC |
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| Abstract | To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor.
Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone
F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm
). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization.
ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10
mm
/sec) was significantly lower than the normal lymph nodes' ADCmean value (1.267 ± 0.88*10
mm
/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10
mm
/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10
mm
/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10
mm
/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05).
DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes.
The trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020. |
|---|---|
| AbstractList | Abstract Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Methods Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone 18F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm2). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization. Results ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10− 3 mm2/sec) was significantly lower than the normal lymph nodes’ ADCmean value (1.267 ± 0.88*10− 3 mm2/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10− 3 mm2/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10− 3 mm2/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10− 3 mm2/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05). Conclusion DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes. Trial registration The trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020. Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Methods Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone 18F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm2). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization. Results ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10− 3 mm2/sec) was significantly lower than the normal lymph nodes’ ADCmean value (1.267 ± 0.88*10− 3 mm2/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10− 3 mm2/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10− 3 mm2/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10− 3 mm2/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05). Conclusion DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes. Trial registration The trial is registered in clinical trials under ID:NCT04360993, registration date: 17/04/2020. Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Methods Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone .sup.18F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm.sup.2). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization. Results ADCmean value of the metastatic lymph nodes in the overall sample (0.899 [+ or -] 0.98*10.sup.- 3 mm.sup.2/sec) was significantly lower than the normal lymph nodes' ADCmean value (1.267 [+ or -] 0.88*10.sup.- 3 mm.sup.2/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 [+ or -] 0.75*10.sup.- 3 mm.sup.2/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 [+ or -] 0.72*10.sup.- 3 mm.sup.2/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 [+ or -] 0.89*10.sup.- 3 mm.sup.2/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05). Conclusion DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes. Trial registration The trial is registered in clinical trials under ID:NCT04360993, registration date: 17/04/2020. Keywords: MRI, DWI, ADC, HNSCC, Metastasis, Benign, Lymph nodes To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm ). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization. ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10 mm /sec) was significantly lower than the normal lymph nodes' ADCmean value (1.267 ± 0.88*10 mm /sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10 mm /sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10 mm /sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10 mm /sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05). DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes. The trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020. To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor.BACKGROUNDTo determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor.Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone 18F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm2). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization.METHODSRetrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone 18F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm2). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization.ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10- 3 mm2/sec) was significantly lower than the normal lymph nodes' ADCmean value (1.267 ± 0.88*10- 3 mm2/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10- 3 mm2/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10- 3 mm2/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10- 3 mm2/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05).RESULTSADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10- 3 mm2/sec) was significantly lower than the normal lymph nodes' ADCmean value (1.267 ± 0.88*10- 3 mm2/sec); (P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10- 3 mm2/sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10- 3 mm2/sec), and the benign lymph nodes ADCmean, (P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10- 3 mm2/sec), (P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, (P > 0.05).DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes.CONCLUSIONDWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes.The trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020.TRIAL REGISTRATIONThe trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020. |
| ArticleNumber | 56 |
| Audience | Academic |
| Author | Cselik, Zsolt Freihat, Omar Pinter, Tamas Sipos, Dávid Kedves, András Repa, Imre Kovács, Árpád |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32771060$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3174/ajnr.A0743 10.1177/0284185115609365 10.12659/PJR.900633 10.1093/annonc/mdq185 10.1002/jmri.22152 10.2214/AJR.12.9432 10.3322/caac.21387 10.1177/1971400917708582 10.1007/s11547-017-0733-y 10.1002/jmri.24994 10.1016/j.ejrad.2012.11.021 10.1016/j.ejrad.2013.12.008 10.1148/radiol.2511080128 10.1002/nbm.1689 10.1097/RCT.0b013e3182405435 10.1148/radiology.182.3.1535890 10.2217/fon.09.77 10.3389/fonc.2019.01362 10.3390/jcm7060130 10.1002/hed.23845 10.1148/radiology.192.3.8058923 10.1148/radiol.2451061804 10.1007/s00259-012-2248-z 10.1148/radiology.175.3.2188292 10.1016/j.clinimag.2011.05.002 10.3348/kjr.2014.15.6.810 10.1016/j.ejrad.2009.07.039 10.2214/AJR.04.1832 10.2967/jnumed.112.105338 10.1016/j.ejrnm.2012.01.008 10.1007/s10334-010-0226-7 10.1016/j.ejrad.2013.01.025 10.3174/ajnr.A3281 10.2967/jnumed.116.180091 10.1016/j.nic.2018.01.010 10.1002/jmri.25405 10.2214/AJR.09.2522 10.1007/s11547-012-0890-x 10.1007/s00259-016-3307-7 10.1007/s00259-016-3361-1 10.1259/bjr/58331222 10.1007/s00330-005-0133-x 10.1148/radiol.2303030157 10.1007/s00234-008-0487-2 |
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| References | S Goldsmid (334_CR39) 2016; 516 NHGM Peters (334_CR31) 2010; 31 JR Anderson (334_CR43) 1985 JP Driessen (334_CR34) 2014; 36 AJG Even (334_CR6) 2016; 43 S Balyasnikova (334_CR13) 2012; 2 C Buchbender (334_CR14) 2012; 53 DM Yousem (334_CR8) 1992; 182 M Sumi (334_CR17) 2006; 186 X Wu (334_CR21) 2013; 82 S Chawla (334_CR44) 2013; 200 HK Lim (334_CR48) 2014; 15 A Das (334_CR24) 2017; 82 AAKA Razek (334_CR10) 2014; 83 JH Rasmussen (334_CR5) 2017; 58 AAK Abdel Razek (334_CR16) 2013; 82 X Wu (334_CR22) 2011; 24 S Ahlawat (334_CR37) 2016; 43 JT Johnson (334_CR4) 1990; 175 F Barchetti (334_CR25) 2014; 2014 A Perrone (334_CR38) 2011; 77 MC Lee (334_CR9) 2013; 34 R Siegel (334_CR1) 2017; 67 KSS Bhatia (334_CR32) 2010; 83 J Si (334_CR33) 2014; 43 N Bhatt (334_CR49) 2017; 30 van den Brekel (334_CR11) 1994; 192 L Kanmaz (334_CR23) 2018; 7 A Srinivasan (334_CR50) 2008; 29 AD King (334_CR51) 2007; 245 S Nakamatsu (334_CR52) 2012; 36 A Srinivasan (334_CR41) 2012; 36 334_CR3 V Grégoire (334_CR2) 2010; 21 AAK Abdel Razek (334_CR15) 2018; 28 A Surov (334_CR28) 2020; 9 X Han (334_CR36) 2017; 45 FPA Pereira (334_CR30) 2009; 193 JH Lee (334_CR27) 2018; 39 AAK Abdel Razek (334_CR35) 2013; 118 S Leibfarth (334_CR7) 2016; 43 De Bondt (334_CR47) 2009; 51 AD King (334_CR18) 2004; 230 334_CR42 TF Taha Ali (334_CR19) 2012; 43 M Lombardi (334_CR29) 2017; 122 CM Wendl (334_CR26) 2016; 57 S Chawla (334_CR20) 2009; 5 I Platzek (334_CR12) 2013; 40 M Sumi (334_CR45) 2003; 24 TC Kwee (334_CR40) 2011; 24 V Vandecaveye (334_CR46) 2009; 251 AAK Abdel Razek (334_CR53) 2006; 16 |
| References_xml | – volume: 29 start-page: 40 issue: 1 year: 2008 ident: 334_CR50 publication-title: Am J Neuroradiol doi: 10.3174/ajnr.A0743 – volume: 57 start-page: 939 issue: 8 year: 2016 ident: 334_CR26 publication-title: Acta Radiol doi: 10.1177/0284185115609365 – volume: 82 start-page: 345 year: 2017 ident: 334_CR24 publication-title: Polish J Radiol doi: 10.12659/PJR.900633 – volume: 21 start-page: 184 issue: SUPPL. 5 year: 2010 ident: 334_CR2 publication-title: Ann Oncol doi: 10.1093/annonc/mdq185 – volume: 2 start-page: 458 issue: 4 year: 2012 ident: 334_CR13 publication-title: Am J Nucl Med Mol Imaging – volume: 31 start-page: 1100 issue: 5 year: 2010 ident: 334_CR31 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.22152 – volume: 200 start-page: 35 issue: 1 year: 2013 ident: 334_CR44 publication-title: Am J Roentgenol doi: 10.2214/AJR.12.9432 – volume: 67 start-page: 7 issue: 1 year: 2017 ident: 334_CR1 publication-title: Ca Cancer J doi: 10.3322/caac.21387 – volume: 30 start-page: 356 issue: 4 year: 2017 ident: 334_CR49 publication-title: Neuroradiol J doi: 10.1177/1971400917708582 – volume: 122 start-page: 345 issue: 5 year: 2017 ident: 334_CR29 publication-title: Radiol Med doi: 10.1007/s11547-017-0733-y – volume: 43 start-page: 446 issue: 2 year: 2016 ident: 334_CR37 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.24994 – volume: 82 start-page: e158 issue: 4 year: 2013 ident: 334_CR21 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2012.11.021 – volume: 83 start-page: 537 issue: 3 year: 2014 ident: 334_CR10 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2013.12.008 – volume: 251 start-page: 134 issue: 1 year: 2009 ident: 334_CR46 publication-title: Radiology doi: 10.1148/radiol.2511080128 – volume: 24 start-page: 1181 issue: 10 year: 2011 ident: 334_CR22 publication-title: NMR Biomed doi: 10.1002/nbm.1689 – volume: 36 start-page: 131 issue: 1 year: 2012 ident: 334_CR41 publication-title: J Comput Assist Tomogr doi: 10.1097/RCT.0b013e3182405435 – volume: 182 start-page: 753 issue: 3 year: 1992 ident: 334_CR8 publication-title: Radiology doi: 10.1148/radiology.182.3.1535890 – volume: 5 start-page: 959 issue: 7 year: 2009 ident: 334_CR20 publication-title: Future Oncol doi: 10.2217/fon.09.77 – volume: 9 start-page: 1 year: 2020 ident: 334_CR28 publication-title: Front Oncol doi: 10.3389/fonc.2019.01362 – volume: 7 start-page: 130 issue: 6 year: 2018 ident: 334_CR23 publication-title: J Clin Med doi: 10.3390/jcm7060130 – volume: 36 start-page: 1391 issue: 10 year: 2014 ident: 334_CR34 publication-title: Head Neck doi: 10.1002/hed.23845 – volume: 192 start-page: 617 issue: 3 year: 1994 ident: 334_CR11 publication-title: Radiology doi: 10.1148/radiology.192.3.8058923 – volume: 24 start-page: 1627 issue: 8 year: 2003 ident: 334_CR45 publication-title: Am J Neuroradiol – volume: 245 start-page: 806 issue: 3 year: 2007 ident: 334_CR51 publication-title: Radiology doi: 10.1148/radiol.2451061804 – volume: 40 start-page: 6 issue: 1 year: 2013 ident: 334_CR12 publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-012-2248-z – volume: 39 start-page: 188 year: 2018 ident: 334_CR27 publication-title: Am J Neuroradiol – volume: 175 start-page: 607 issue: 3 year: 1990 ident: 334_CR4 publication-title: Radiology doi: 10.1148/radiology.175.3.2188292 – volume: 36 start-page: 90 issue: 2 year: 2012 ident: 334_CR52 publication-title: Clin Imaging doi: 10.1016/j.clinimag.2011.05.002 – volume-title: Muir’s textbook of pathology year: 1985 ident: 334_CR43 – volume: 15 start-page: 810 issue: 6 year: 2014 ident: 334_CR48 publication-title: Korean J Radiol doi: 10.3348/kjr.2014.15.6.810 – volume: 77 start-page: 281 issue: 2 year: 2011 ident: 334_CR38 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2009.07.039 – volume: 516 start-page: 1 year: 2016 ident: 334_CR39 publication-title: Trends Issues Crime Crim Justice – volume: 186 start-page: 749 issue: 3 year: 2006 ident: 334_CR17 publication-title: Am J Roentgenol doi: 10.2214/AJR.04.1832 – volume: 53 start-page: 928 issue: 6 year: 2012 ident: 334_CR14 publication-title: J Nucl Med doi: 10.2967/jnumed.112.105338 – volume: 2014 start-page: 532095 year: 2014 ident: 334_CR25 publication-title: Biomed Res Int – volume: 43 start-page: 173 issue: 2 year: 2012 ident: 334_CR19 publication-title: Egypt J Radiol Nucl Med doi: 10.1016/j.ejrnm.2012.01.008 – volume: 24 start-page: 1 issue: 1 year: 2011 ident: 334_CR40 publication-title: Magn Reson Mater Physics, Biol Med doi: 10.1007/s10334-010-0226-7 – volume: 82 start-page: 982 issue: 6 year: 2013 ident: 334_CR16 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2013.01.025 – ident: 334_CR3 – volume: 34 start-page: 864 issue: 4 year: 2013 ident: 334_CR9 publication-title: Am J Neuroradiol doi: 10.3174/ajnr.A3281 – ident: 334_CR42 – volume: 58 start-page: 69 issue: 1 year: 2017 ident: 334_CR5 publication-title: J Nucl Med doi: 10.2967/jnumed.116.180091 – volume: 28 start-page: 245 issue: 2 year: 2018 ident: 334_CR15 publication-title: Neuroimaging Clin N Am doi: 10.1016/j.nic.2018.01.010 – volume: 45 start-page: 722 issue: 3 year: 2017 ident: 334_CR36 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.25405 – volume: 193 start-page: 1030 issue: 4 year: 2009 ident: 334_CR30 publication-title: Am J Roentgenol doi: 10.2214/AJR.09.2522 – volume: 118 start-page: 534 issue: 4 year: 2013 ident: 334_CR35 publication-title: Radiol Med doi: 10.1007/s11547-012-0890-x – volume: 43 start-page: 1199 issue: 7 year: 2016 ident: 334_CR7 publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-016-3307-7 – volume: 43 start-page: 1195 issue: 7 year: 2016 ident: 334_CR6 publication-title: Eur J Nucl Med Mol Imaging doi: 10.1007/s00259-016-3361-1 – volume: 83 start-page: 753 issue: 993 year: 2010 ident: 334_CR32 publication-title: Br J Radiol doi: 10.1259/bjr/58331222 – volume: 16 start-page: 1468 issue: 7 year: 2006 ident: 334_CR53 publication-title: Eur Radiol doi: 10.1007/s00330-005-0133-x – volume: 230 start-page: 720 issue: 3 year: 2004 ident: 334_CR18 publication-title: Radiology doi: 10.1148/radiol.2303030157 – volume: 43 start-page: 1 issue: 3 year: 2014 ident: 334_CR33 publication-title: Dentomaxillofacial Radiol – volume: 51 start-page: 183 issue: 3 year: 2009 ident: 334_CR47 publication-title: Neuroradiology doi: 10.1007/s00234-008-0487-2 |
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| Snippet | To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation... Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the... Abstract Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes... |
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| SubjectTerms | ADC Adult Aged Benign Biopsy Cancer metastasis Cancer therapies Care and treatment Chemotherapy Diffusion Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods Driving while intoxicated DWI Female Head & neck cancer Head and Neck Neoplasms - diagnostic imaging Head and Neck Neoplasms - pathology HNSCC Humans Imaging techniques Lymph Nodes - diagnostic imaging Lymph Nodes - pathology Lymphatic Metastasis Lymphatic system Magnetic resonance imaging Male Medical imaging Metastasis Middle Aged MRI Multimodal Imaging - methods Patients Positron emission Positron-Emission Tomography - methods Squamous cell carcinoma Squamous Cell Carcinoma of Head and Neck - diagnostic imaging Squamous Cell Carcinoma of Head and Neck - pathology Studies Surgery Tomography Tumors |
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| Title | Diffusion-Weighted Imaging (DWI) derived from PET/MRI for lymph node assessment in patients with Head and Neck Squamous Cell Carcinoma (HNSCC) |
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