Evidence map›Paper›PMID 36212484›Full record

ArticleFrontiers in oncology2022

Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma.

Qing-Cong Kong, Wen-Jie Tang, Si-Yi Chen, Wen-Ke Hu, Yue Hu, Yun-Shi Liang, Qiong-Qiong Zhang, Zi-Xuan Cheng, Di Huang, Jing Yang and 1 more

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Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Qing-Cong KongDepartment of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Wen-Jie TangDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Si-Yi ChenDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Wen-Ke HuDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Yue HuBreast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Yun-Shi LiangDepartment of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Qiong-Qiong ZhangDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Zi-Xuan ChengDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Di HuangDepartment of Breast Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Jing YangDepartment of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Yuan GuoDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC). Methods: We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method. Results: Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29-65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28-88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750-0.949) in the training set and 0.819 (95% CI: 0.693-0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035). Conclusions: In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.

Indexed as

histological subtypesmagnetic resonance imagingmetaplastic breast carcinomanomogramstriple negative breast cancer

Identifiers

PMID36212484
PMCPMC9533710

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