View in EDS HTML Full Text PDF Full Text

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.

Saved in:
Bibliographic Details
Title: Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.
Authors: Li, Yanbo1,2 (AUTHOR), Chen, Yongzi2,3 (AUTHOR), Zhao, Rui1,2 (AUTHOR), Ji, Yu1,2 (AUTHOR), Li, Junnan1,2 (AUTHOR), Zhang, Ying1,2 (AUTHOR), Lu, Hong1,2 (AUTHOR) honglu@tmu.edu.cn
Source: European Radiology. Mar2022, Vol. 32 Issue 3, p1676-1687. 12p.
Subjects: Triple-negative breast cancer, Neoadjuvant chemotherapy, Tumor growth, Androgen receptors, Prediction models, Pathologic complete response, Contrast-enhanced magnetic resonance imaging, Nomography (Mathematics)
Abstract: Objectives: To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).Methods: A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.Results: Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.Conclusions: Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.Key Points: • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients. [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: 155182494
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Yanbo%22">Li, Yanbo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yongzi%22">Chen, Yongzi</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Rui%22">Zhao, Rui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ji%2C+Yu%22">Ji, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Junnan%22">Li, Junnan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Ying%22">Zhang, Ying</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Hong%22">Lu, Hong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> honglu@tmu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Mar2022, Vol. 32 Issue 3, p1676-1687. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Triple-negative+breast+cancer%22">Triple-negative breast cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Neoadjuvant+chemotherapy%22">Neoadjuvant chemotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+growth%22">Tumor growth</searchLink><br /><searchLink fieldCode="DE" term="%22Androgen+receptors%22">Androgen receptors</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Pathologic+complete+response%22">Pathologic complete response</searchLink><br /><searchLink fieldCode="DE" term="%22Contrast-enhanced+magnetic+resonance+imaging%22">Contrast-enhanced magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Nomography+%28Mathematics%29%22">Nomography (Mathematics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: <bold>Objectives: </bold>To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).<bold>Methods: </bold>A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.<bold>Results: </bold>Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.<bold>Conclusions: </bold>Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.<bold>Key Points: </bold>• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=155182494
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00330-021-08291-0
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1676
    Subjects:
      – SubjectFull: Triple-negative breast cancer
        Type: general
      – SubjectFull: Neoadjuvant chemotherapy
        Type: general
      – SubjectFull: Tumor growth
        Type: general
      – SubjectFull: Androgen receptors
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Pathologic complete response
        Type: general
      – SubjectFull: Contrast-enhanced magnetic resonance imaging
        Type: general
      – SubjectFull: Nomography (Mathematics)
        Type: general
    Titles:
      – TitleFull: Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Yanbo
      – PersonEntity:
          Name:
            NameFull: Chen, Yongzi
      – PersonEntity:
          Name:
            NameFull: Zhao, Rui
      – PersonEntity:
          Name:
            NameFull: Ji, Yu
      – PersonEntity:
          Name:
            NameFull: Li, Junnan
      – PersonEntity:
          Name:
            NameFull: Zhang, Ying
      – PersonEntity:
          Name:
            NameFull: Lu, Hong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 09387994
          Numbering:
            – Type: volume
              Value: 32
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: European Radiology
              Type: main
ResultId 1