View in EDS HTML Full Text PDF Full Text

The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.

Saved in:
Bibliographic Details
Title: The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.
Authors: Qu, Jinrong1,2, Shen, Chen2,3, Qin, Jianjun4, Wang, Zhaoqi1, Liu, Zhenyu3, Guo, Jia1, Zhang, Hongkai1, Gao, Pengrui1, Bei, Tianxia1, Wang, Yingshu1, Liu, Hui1, Kamel, Ihab R.5, Tian, Jie2,3 jie.tian@ia.ac.cn, Li, Hailiang1 doctorhnchr@126.com
Source: European Radiology. Feb2019, Vol. 29 Issue 2, p906-914. 9p. 1 Black and White Photograph, 1 Diagram, 2 Charts, 4 Graphs.
Subjects: Lymph node diseases, Metastasis, Esophageal cancer, Magnetic resonance imaging, Cancer treatment
Abstract: Purpose: To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC).Patients and Methods: A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts.Results: Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes.Conclusion: The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients.Key Points: • The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 133695300
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Qu%2C+Jinrong%22&quot;&gt;Qu, Jinrong&lt;/searchLink&gt;&lt;relatesTo&gt;1,2&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Shen%2C+Chen%22&quot;&gt;Shen, Chen&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Qin%2C+Jianjun%22&quot;&gt;Qin, Jianjun&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wang%2C+Zhaoqi%22&quot;&gt;Wang, Zhaoqi&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Liu%2C+Zhenyu%22&quot;&gt;Liu, Zhenyu&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Guo%2C+Jia%22&quot;&gt;Guo, Jia&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhang%2C+Hongkai%22&quot;&gt;Zhang, Hongkai&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Gao%2C+Pengrui%22&quot;&gt;Gao, Pengrui&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Bei%2C+Tianxia%22&quot;&gt;Bei, Tianxia&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wang%2C+Yingshu%22&quot;&gt;Wang, Yingshu&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Liu%2C+Hui%22&quot;&gt;Liu, Hui&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kamel%2C+Ihab+R%2E%22&quot;&gt;Kamel, Ihab R.&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Tian%2C+Jie%22&quot;&gt;Tian, Jie&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt;&lt;i&gt; jie.tian@ia.ac.cn&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Li%2C+Hailiang%22&quot;&gt;Li, Hailiang&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;i&gt; doctorhnchr@126.com&lt;/i&gt;
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Feb2019, Vol. 29 Issue 2, p906-914. 9p. 1 Black and White Photograph, 1 Diagram, 2 Charts, 4 Graphs.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Lymph+node+diseases%22&quot;&gt;Lymph node diseases&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Metastasis%22&quot;&gt;Metastasis&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Esophageal+cancer%22&quot;&gt;Esophageal cancer&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Magnetic+resonance+imaging%22&quot;&gt;Magnetic resonance imaging&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Cancer+treatment%22&quot;&gt;Cancer treatment&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: &lt;bold&gt;Purpose: &lt;/bold&gt;To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC).&lt;bold&gt;Patients and Methods: &lt;/bold&gt;A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts.&lt;bold&gt;Results: &lt;/bold&gt;Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p &lt; 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes.&lt;bold&gt;Conclusion: &lt;/bold&gt;The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients.&lt;bold&gt;Key Points: &lt;/bold&gt;• The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=133695300
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00330-018-5583-z
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 906
    Subjects:
      – SubjectFull: Lymph node diseases
        Type: general
      – SubjectFull: Metastasis
        Type: general
      – SubjectFull: Esophageal cancer
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Cancer treatment
        Type: general
    Titles:
      – TitleFull: The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Qu, Jinrong
      – PersonEntity:
          Name:
            NameFull: Shen, Chen
      – PersonEntity:
          Name:
            NameFull: Qin, Jianjun
      – PersonEntity:
          Name:
            NameFull: Wang, Zhaoqi
      – PersonEntity:
          Name:
            NameFull: Liu, Zhenyu
      – PersonEntity:
          Name:
            NameFull: Guo, Jia
      – PersonEntity:
          Name:
            NameFull: Zhang, Hongkai
      – PersonEntity:
          Name:
            NameFull: Gao, Pengrui
      – PersonEntity:
          Name:
            NameFull: Bei, Tianxia
      – PersonEntity:
          Name:
            NameFull: Wang, Yingshu
      – PersonEntity:
          Name:
            NameFull: Liu, Hui
      – PersonEntity:
          Name:
            NameFull: Kamel, Ihab R.
      – PersonEntity:
          Name:
            NameFull: Tian, Jie
      – PersonEntity:
          Name:
            NameFull: Li, Hailiang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 09387994
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: European Radiology
              Type: main
ResultId 1