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Ultrasound-based radiomics-clinical nomogram for noninvasive prediction of residual cancer burden grading in breast cancer.
Li, Zhi-Yong; Wu, Sheng-Nan; Lin, Zhen-Hu; Jiang, Mei-Chen; Chen, Cong; Liang, Rong-Xi; Lin, Wen-Jin; Xue, En-Sheng.
Afiliação
  • Li ZY; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
  • Wu SN; Department of Ultrasound, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Lin ZH; Department of Ultrasound, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Jiang MC; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
  • Chen C; Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China.
  • Liang RX; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
  • Lin WJ; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
  • Xue ES; Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
J Clin Ultrasound ; 52(5): 566-574, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38538081
ABSTRACT

PURPOSE:

To assess the predictive value of an ultrasound-based radiomics-clinical nomogram for grading residual cancer burden (RCB) in breast cancer patients.

METHODS:

This retrospective study of breast cancer patients who underwent neoadjuvant therapy (NAC) and ultrasound scanning between November 2020 and July 2023. First, a radiomics model was established based on ultrasound images. Subsequently, multivariate LR (logistic regression) analysis incorporating both radiomic scores and clinical factors was performed to construct a nomogram. Finally, Receiver operating characteristics (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate and validate the diagnostic accuracy and effectiveness of the nomogram.

RESULTS:

A total of 1122 patients were included in this study. Among them, 427 patients exhibited a favorable response to NAC chemotherapy, while 695 patients demonstrated a poor response to NAC therapy. The radiomics model achieved an AUC value of 0.84 in the training cohort and 0.83 in the validation cohort. The ultrasound-based radiomics-clinical nomogram achieved an AUC value of 0.90 in the training cohort and 0.91 in the validation cohort.

CONCLUSIONS:

Ultrasound-based radiomics-clinical nomogram can accurately predict the effectiveness of NAC therapy by predicting RCB grading in breast cancer patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Ultrassonografia Mamária / Neoplasia Residual / Nomogramas / Gradação de Tumores Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Ultrassonografia Mamária / Neoplasia Residual / Nomogramas / Gradação de Tumores Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article