Intra‐ and Peri‐tumoral Radiomics Based on Dynamic Contrast Enhanced ‐MRI to Identify Lymph Node Metastasis and Prognosis in Intrahepatic Cholangiocarcinoma
Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM. To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images fo...
Saved in:
Published in | Journal of magnetic resonance imaging Vol. 60; no. 6; pp. 2669 - 2680 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Wiley Subscription Services, Inc
01.12.2024
|
Subjects | |
Online Access | Get full text |
ISSN | 1053-1807 1522-2586 1522-2586 |
DOI | 10.1002/jmri.29390 |
Cover
Abstract | Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM.
To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA.
Retrospective.
Two hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51).
T1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T.
Radiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated.
Receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant.
The nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS.
The nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA.
3 TECHNICAL EFFICACY: Stage 2. |
---|---|
AbstractList | Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM.BACKGROUNDLymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM.To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA.PURPOSETo develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA.Retrospective.STUDY TYPERetrospective.Two hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51).SUBJECTSTwo hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51).T1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T.FIELD STRENGTH/SEQUENCET1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T.Radiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated.ASSESSMENTRadiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated.Receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant.STATISTICAL TESTSReceiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant.The nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS.RESULTSThe nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS.The nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA.DATA CONCLUSIONThe nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA.3 TECHNICAL EFFICACY: Stage 2.EVIDENCE LEVEL3 TECHNICAL EFFICACY: Stage 2. Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM. To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA. Retrospective. Two hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51). T1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T. Radiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated. Receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant. The nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS. The nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA. 3 TECHNICAL EFFICACY: Stage 2. BackgroundLymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM.PurposeTo develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)‐MR images for identifying LNM and prognosis in iCCA.Study TypeRetrospective.SubjectsTwo hundred four patients with pathologically proven iCCA who underwent curative‐intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51).Field Strength/SequenceT1‐ and T2‐weighted imaging, diffusion‐weighted imaging and DCE imaging at 1.5 T or 3.0 T.AssessmentRadiomics features were extracted from intra‐ and peri‐tumoral regions on preoperative DCE‐MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best‐performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI‐reported LNM) were combined to build a nomogram. Patients were divided into high‐risk and low‐risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1–102 months (median 12) after surgery, the overall survival (OS) and recurrence‐free survival (RFS) were calculated.Statistical TestsReceiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan–Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant.ResultsThe nomogram incorporating intra‐ and peri‐tumoral radiomics features, intrahepatic duct dilatation and MRI‐reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high‐risk patients had significantly lower RFS and OS than low‐risk patients. High‐risk of LNM was an independent factor of unfavorable OS and RFS.Data ConclusionThe nomogram integrating intra‐ and peri‐tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA.Evidence Level3Technical EfficacyStage 2 |
Author | Wu, Sun‐jie Xu, Peng‐Ju Shan, Yan Lin, Jiang Zeng, Yan Cao, Zi‐Rui Pan, Yi‐Jun |
Author_xml | – sequence: 1 givenname: Yi‐Jun surname: Pan fullname: Pan, Yi‐Jun organization: Department of Radiology, Zhongshan Hospital Fudan University Shanghai China, Shanghai Institute of Medical Imaging Shanghai China – sequence: 2 givenname: Sun‐jie surname: Wu fullname: Wu, Sun‐jie organization: Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou Zhejiang China – sequence: 3 givenname: Yan surname: Zeng fullname: Zeng, Yan organization: Department of Research Center Shanghai United Imaging Intelligence Co., Ltd. Shanghai China – sequence: 4 givenname: Zi‐Rui surname: Cao fullname: Cao, Zi‐Rui organization: Department of Research Center Shanghai United Imaging Intelligence Co., Ltd. Shanghai China – sequence: 5 givenname: Yan surname: Shan fullname: Shan, Yan organization: Department of Radiology, Zhongshan Hospital Fudan University Shanghai China, Shanghai Institute of Medical Imaging Shanghai China – sequence: 6 givenname: Jiang surname: Lin fullname: Lin, Jiang organization: Department of Radiology, Zhongshan Hospital Fudan University Shanghai China, Shanghai Institute of Medical Imaging Shanghai China – sequence: 7 givenname: Peng‐Ju orcidid: 0000-0002-0832-083X surname: Xu fullname: Xu, Peng‐Ju organization: Department of Radiology, Zhongshan Hospital Fudan University Shanghai China, Shanghai Institute of Medical Imaging Shanghai China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38609076$$D View this record in MEDLINE/PubMed |
BookMark | eNptkc1u1DAUhS3Uiv7AhgdAltggpLT-S-IsYSgw0hRQBevIsW86HiX21HYWs-MReAYejSfBmSldVF352vc710f3nKEj5x0g9IqSC0oIu9yMwV6whjfkGTqlJWMFK2V1lGtS8oJKUp-gsxg3hJCmEeVzdMJlRRpSV6foz9KloP7--o2VM_g7BJvrNI0-qAHfKGP9aHXEH1QEg73DH3dO5Re88LMuJnzl1srp3My665slTh4vDbhk-x1e7cbtGn_1BvA1pEyraOPho-BvnZ9v1uG9hTVsVZoHr_2g3K31WgVtnR_VC3TcqyHCy_vzHP38dPVj8aVYffu8XLxfFZrTMhVK6Y6CURqaTouyrpRmVBnW6x4kyB5KCZwBgDCEiE7L3BG6rxojOzB1xc_R28PcbfB3E8TUjjZqGLId8FNsOeFSCEqFyOibR-jGT8Fldy2nTLKqFpxn6vU9NXUjmHYb7KjCrv2__Qy8OwA6-BgD9A8IJe0cbTtH2-6jzTB5BGub8sr2QdjhKck_WP2tRA |
CitedBy_id | crossref_primary_10_1002_jmri_29478 crossref_primary_10_1016_j_acra_2024_09_035 crossref_primary_10_20517_2394_5079_2024_79 crossref_primary_10_1007_s11547_024_01910_y crossref_primary_10_3390_cancers16111946 crossref_primary_10_1016_j_radonc_2025_110770 crossref_primary_10_1002_jmri_29391 |
Cites_doi | 10.21037/hbsn.2017.01.06 10.1016/j.jhep.2014.01.021 10.1088/1361-6560/ac01f3 10.1371/journal.pone.0229292 10.1016/j.ejso.2019.11.511 10.1007/s10620-008-0408-6 10.1148/radiol.220329 10.1002/cncr.29936 10.3322/caac.21708 10.1200/JCO.2011.35.6519 10.1002/jso.25450 10.1007/s11605-021-05039-5 10.1016/j.eclinm.2021.101215 10.1001/jamasurg.2013.5137 10.3389/fonc.2021.585808 10.1245/s10434-014-4239-8 10.1001/jamaoncol.2017.3055 10.1007/s12072-022-10477-7 10.1016/j.jhep.2022.10.021 10.1007/s00330-019-06142-7 10.1038/s41422-023-00831-1 10.1007/s00259-022-05765-1 10.1200/JCO.18.02178 10.1148/radiol.2018181408 10.1097/RCT.0000000000000695 10.1002/bjs.5920 10.1001/jamasurg.2020.1973 10.7150/thno.34149 10.1007/s00330-018-5898-9 10.1001/jamanetworkopen.2020.15927 |
ContentType | Journal Article |
Copyright | 2024 International Society for Magnetic Resonance in Medicine. 2024 International Society for Magnetic Resonance in Medicine |
Copyright_xml | – notice: 2024 International Society for Magnetic Resonance in Medicine. – notice: 2024 International Society for Magnetic Resonance in Medicine |
DBID | AAYXX CITATION NPM 7QO 7TK 8FD FR3 K9. P64 7X8 |
DOI | 10.1002/jmri.29390 |
DatabaseName | CrossRef PubMed Biotechnology Research Abstracts Neurosciences Abstracts Technology Research Database Engineering Research Database ProQuest Health & Medical Complete (Alumni) Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitle | CrossRef PubMed ProQuest Health & Medical Complete (Alumni) Engineering Research Database Biotechnology Research Abstracts Technology Research Database Neurosciences Abstracts Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic PubMed ProQuest Health & Medical Complete (Alumni) |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 1522-2586 |
EndPage | 2680 |
ExternalDocumentID | 38609076 10_1002_jmri_29390 |
Genre | Journal Article |
GrantInformation_xml | – fundername: Science and Technology Commission of Shanghai Municipality grantid: 22Y11910900 – fundername: Shanghai Municipal Health Commission grantid: shslczdzk03202 |
GroupedDBID | --- -DZ .3N .GA .GJ .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 31~ 33P 3O- 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 53G 5GY 5RE 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHHS AAHQN AAIPD AAMNL AANHP AANLZ AAONW AASGY AAWTL AAXRX AAYCA AAYXX AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABLJU ABOCM ABPVW ABQWH ABXGK ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACGOF ACIWK ACMXC ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUYR AEYWJ AFBPY AFFPM AFGKR AFRAH AFWVQ AFZJQ AGHNM AGQPQ AGYGG AHBTC AHMBA AIACR AIAGR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 C45 CITATION CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 F5P FEDTE FUBAC G-S G.N GNP GODZA H.X HBH HDBZQ HF~ HGLYW HHY HHZ HVGLF HZ~ IX1 J0M JPC KBYEO KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M65 MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG OVD P2P P2W P2X P2Z P4B P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K RIWAO RJQFR ROL RX1 RYL SAMSI SUPJJ SV3 TEORI TWZ UB1 V2E V8K V9Y W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WIN WJL WOHZO WQJ WVDHM WXI WXSBR XG1 XV2 ZXP ZZTAW ~IA ~WT AAMMB AEFGJ AGXDD AIDQK AIDYY NPM 7QO 7TK 8FD FR3 K9. P64 7X8 |
ID | FETCH-LOGICAL-c315t-aacb1edace9bc4576ac21ad2fcfe8e8fe58e32eee4d004bc82fc4cf69d8bed763 |
ISSN | 1053-1807 1522-2586 |
IngestDate | Fri Jul 11 04:50:36 EDT 2025 Fri Jul 25 12:18:57 EDT 2025 Mon Jul 21 05:39:20 EDT 2025 Thu Apr 24 23:06:17 EDT 2025 Tue Jul 01 03:57:03 EDT 2025 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6 |
Keywords | radiomics cholangiocarcinoma prognosis lymph node metastasis |
Language | English |
License | 2024 International Society for Magnetic Resonance in Medicine. |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c315t-aacb1edace9bc4576ac21ad2fcfe8e8fe58e32eee4d004bc82fc4cf69d8bed763 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0002-0832-083X |
PMID | 38609076 |
PQID | 3128267433 |
PQPubID | 1006400 |
PageCount | 12 |
ParticipantIDs | proquest_miscellaneous_3038441144 proquest_journals_3128267433 pubmed_primary_38609076 crossref_primary_10_1002_jmri_29390 crossref_citationtrail_10_1002_jmri_29390 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2024-12-00 2024-Dec 20241201 |
PublicationDateYYYYMMDD | 2024-12-01 |
PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-00 |
PublicationDecade | 2020 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: Nashville |
PublicationSubtitle | JMRI |
PublicationTitle | Journal of magnetic resonance imaging |
PublicationTitleAlternate | J Magn Reson Imaging |
PublicationYear | 2024 |
Publisher | Wiley Subscription Services, Inc |
Publisher_xml | – name: Wiley Subscription Services, Inc |
References | e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_28_1 e_1_2_7_29_1 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_21_1 e_1_2_7_20_1 |
References_xml | – ident: e_1_2_7_22_1 doi: 10.21037/hbsn.2017.01.06 – ident: e_1_2_7_4_1 doi: 10.1016/j.jhep.2014.01.021 – ident: e_1_2_7_18_1 doi: 10.1088/1361-6560/ac01f3 – ident: e_1_2_7_25_1 doi: 10.1371/journal.pone.0229292 – ident: e_1_2_7_29_1 doi: 10.1016/j.ejso.2019.11.511 – ident: e_1_2_7_23_1 doi: 10.1007/s10620-008-0408-6 – ident: e_1_2_7_16_1 doi: 10.1148/radiol.220329 – ident: e_1_2_7_2_1 doi: 10.1002/cncr.29936 – ident: e_1_2_7_5_1 doi: 10.3322/caac.21708 – ident: e_1_2_7_8_1 doi: 10.1200/JCO.2011.35.6519 – ident: e_1_2_7_20_1 doi: 10.1002/jso.25450 – ident: e_1_2_7_21_1 doi: 10.1007/s11605-021-05039-5 – ident: e_1_2_7_27_1 doi: 10.1016/j.eclinm.2021.101215 – ident: e_1_2_7_6_1 doi: 10.1001/jamasurg.2013.5137 – ident: e_1_2_7_9_1 doi: 10.3389/fonc.2021.585808 – ident: e_1_2_7_11_1 doi: 10.1245/s10434-014-4239-8 – ident: e_1_2_7_3_1 doi: 10.1001/jamaoncol.2017.3055 – ident: e_1_2_7_19_1 doi: 10.1007/s12072-022-10477-7 – ident: e_1_2_7_10_1 doi: 10.1016/j.jhep.2022.10.021 – ident: e_1_2_7_13_1 doi: 10.1007/s00330-019-06142-7 – ident: e_1_2_7_26_1 doi: 10.1038/s41422-023-00831-1 – ident: e_1_2_7_17_1 doi: 10.1007/s00259-022-05765-1 – ident: e_1_2_7_24_1 doi: 10.1200/JCO.18.02178 – ident: e_1_2_7_15_1 doi: 10.1148/radiol.2018181408 – ident: e_1_2_7_31_1 doi: 10.1097/RCT.0000000000000695 – ident: e_1_2_7_12_1 doi: 10.1002/bjs.5920 – ident: e_1_2_7_7_1 doi: 10.1001/jamasurg.2020.1973 – ident: e_1_2_7_14_1 doi: 10.7150/thno.34149 – ident: e_1_2_7_30_1 doi: 10.1007/s00330-018-5898-9 – ident: e_1_2_7_28_1 doi: 10.1001/jamanetworkopen.2020.15927 |
SSID | ssj0009945 |
Score | 2.4895692 |
Snippet | Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging... BackgroundLymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative... |
SourceID | proquest pubmed crossref |
SourceType | Aggregation Database Index Database Enrichment Source |
StartPage | 2669 |
SubjectTerms | Cholangiocarcinoma Field strength Image contrast Image enhancement Lymph nodes Lymphatic system Magnetic resonance imaging Medical imaging Medical prognosis Metastases Metastasis Nomograms Patients Prognosis Radiomics Rank tests Regression analysis Regression models Risk Risk groups Statistical analysis Statistical tests Survival Training |
Title | Intra‐ and Peri‐tumoral Radiomics Based on Dynamic Contrast Enhanced ‐MRI to Identify Lymph Node Metastasis and Prognosis in Intrahepatic Cholangiocarcinoma |
URI | https://www.ncbi.nlm.nih.gov/pubmed/38609076 https://www.proquest.com/docview/3128267433 https://www.proquest.com/docview/3038441144 |
Volume | 60 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3LbtQwFLWGIiE2qLyHFmQEGzTKMHHeSxiK2orpYtSKtpvIsZ02FZNUM8mirPgEvoFP4xP4Aq4fcWaYIhU2UZTYcZJ7cu51fB8Ivc5YBt-By5xQJrkDfc0cGgAZ0ojyKM8jkccyOHlyEO4e-fvHwXGv92vJa6mpsyH7em1cyf9IFY6BXGWU7D9I1l4UDsA-yBe2IGHY3kjGe_LPrHVX0I7_MnS8PVI3MxWAP6W8kNHHi8F7UFpcLhB80JXoZcQfXGNRD3bKc-0MYHtPpnvSMtWhvPnV4NMVSH5wUHFgAlFDHyqTmZhoA-mvVyjfWnVT5-JSpYIdy6lzeVaAxpyzoqyMFli3hmf0rBQ6n7ScHEi6KWaqglK3yKUI8qR7vP3GIvtzo12MSnvyorCQPRWa0E66L2FM1S_i0-5i06ZY_gNC_CVvEk3aQCSOG-vquUNhiBwm2SQwabYN0-vKBQbRK7Qd6noxxgQgoa4utaZedLrai9m8GIKZpOucrubw_kO3Wo9HnR2apLJvqvreQrdJBPaeNOSnXcqzJFGFte0z2ZS65G037qoR9ZeZkbKQDjfRPSNM_E7j9D7qifIBujMxzhsP0Q-FjJ_fvmPADJZAhX0DUWwhihVEcVViA1HcQhS3EMXQD8CJ6wq34MQKnFiCE3fg1AO14MRFiZfBidfB-Qgdfdw5HO86pkSIwzw3qB1KWeYKTplIMubD3Jky4lJOcpaLWMS5CGLhESGEz4E3MhbDGZ_lYcLjTHDQrY_RRlmV4inCPvcYiwI_yuPIJ65PCQs42Hc8GdF8FCd99KZ95ykz-fNlGZcv6bps--iVbXups8Zc22q7FV1qWGWRemAwEhkZ5PXRS3saOF8u5NFSVA20GXkxTGNc3--jJ1rkdhgvDkfJKAqf3egWttDd7ovaRhv1vBHPwcqusxcKlr8B7HzWdg |
linkProvider | Wiley-Blackwell |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Intra%E2%80%90+and+Peri%E2%80%90tumoral+Radiomics+Based+on+Dynamic+Contrast+Enhanced+%E2%80%90MRI+to+Identify+Lymph+Node+Metastasis+and+Prognosis+in+Intrahepatic+Cholangiocarcinoma&rft.jtitle=Journal+of+magnetic+resonance+imaging&rft.au=Pan%2C+Yi%E2%80%90Jun&rft.au=Wu%2C+Sun%E2%80%90jie&rft.au=Zeng%2C+Yan&rft.au=Cao%2C+Zi%E2%80%90Rui&rft.date=2024-12-01&rft.issn=1053-1807&rft.eissn=1522-2586&rft.volume=60&rft.issue=6&rft.spage=2669&rft.epage=2680&rft_id=info:doi/10.1002%2Fjmri.29390&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_jmri_29390 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-1807&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-1807&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-1807&client=summon |