Evidence map›Paper›PMID 36034380›Full record

ArticleFrontiers in surgery2022

A nomogram based on clinical factors for preoperative prediction of nipple involvement in breast cancer.

Weiling Huang, Zhikun Qiu, Tai Mu, Xi Li

Abstract read
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Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Weiling HuangDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Zhikun QiuDepartment of Breast Surgery, Huizhou Central People's Hospital, Huizhou, China.
Tai MuDepartment of Thyroid and Breast Surgery, The First People's Hospital of Kashgar, Xinjiang, China.
Xi LiDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: At present, the indication for nipple-sparing mastectomy (NSM) remains inconclusive, and occult nipple involvement (NI) is one of the most important problems when carrying out NSM. Therefore, we aimed to identify the predictive factors of NI, to provide a tool for selecting suitable candidates for NSM. Methods: In this retrospective study, a total of 250 breast cancer patients who received mastectomy were recruited, and the association between NI and tumor clinicopathologic characteristics was investigated. Nipple signs, tumor size measured by ultrasound (US), and tumor location were developed as a nomogram to predict NI. Results: Among the 250 patients, 34 (12.6%) had NI, and 216 (86.4%) did not. In the training group, NI was associated with nipple signs, tumor size, tumor-nipple distance (TND), tumor location, lymph node metastasis, and HER2 overexpression. Both in the training and in the validation groups, NI showed a significant association with nipple signs, tumor size measured by ultrasound, and tumor location. Based on these three clinical factors, the preoperative model nomogram was proved to have high efficiency in predicting NI, possessing a sensitivity of 80.0% and a specificity of 86.7% in the validation group. Conclusions: We proposed a predictive model nomogram utilizing preoperative tumor characteristics, including nipple signs, tumor size measured by ultrasound, and tumor location. This predictive model could help in the planning of nipple-sparing mastectomy.

Indexed as

breast cancerclinicopathologic characteristicsnipple involvementnipple-sparing mastectomypredictive

Identifiers

PMID36034380
PMCPMC9403123

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