Evidence map›Paper›PMID 40225004›Full record

ArticleTranslational cancer research2025

Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.

Zhe Yang, Shouen Wang, Wei Yin, Ying Wang, Fanghua Liu, Jianshu Xu, Long Han, Chenglong Liu

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Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Zhe YangDepartment of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Shouen WangDepartment of Pathology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Wei YinDepartment of Radiology, Beijing Friendship Hospital of Capital Medical University, Beijing, China.
Ying WangDepartment of Radiology, the First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Fanghua LiuDepartment of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Jianshu XuDepartment of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Long HanDepartment of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Chenglong LiuDepartment of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models. Results: Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures. Conclusions: Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.

Indexed as

breast cancermagnetic resonance imaging (MRI)nomogramRadiomicstumor-node-metastasis (TNM)

Identifiers

PMID40225004
PMCPMC11985186

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