Evidence map›Paper›PMID 40597882›Full record

ArticleBMC cancer2025

Prediction of the 70-gene signature (MammaPrint) high versus low risk by nomograms among axillary lymph node positive (LN+) and negative (LN-) Chinese breast cancer patients, a retrospective study.

Jie Lian, Ru Yao, Ying Xu, Linjuan Tan, Fangyuan Chen, Jiahui Zhang, Yang Qu, Lu Gao, Yanna Zhang, Songjie Shen and 6 more

Abstract read
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Artificial Intelligence in Medicine: A Definition of Terms.Journal of community hospital internal medicine perspectives · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Jie Lian *Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Ru Yao *Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Ying Xu *Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Linjuan Tan *Department of Anesthesia and Operating Room, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Fangyuan ChenDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Jiahui ZhangDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Yang QuDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Lu GaoDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Yanna ZhangDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Songjie ShenDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Qingli ZhuDepartment of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Xinyu RenDepartment of Pathology and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Lingyan KongDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China.
Bo PanDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. panbo@pumch.cn.
Qiang SunDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Yidong ZhouDepartment of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. zhouyd@pumch.cn.

Funding

National High Level Hospital Clinical Research Funding 2022-PUMCH-B-039National High Level Hospital Clinical Research Funding Grant No. 2022-PUMCH-A-165the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) 2021-I2M-1-014the Natural Science Foundation of China No. 81001183
6 · The paper itself

Abstract

backgroundLuminal-type breast cancer (BC) was characterized as hormonal receptor positive human epidermal receptor 2 negative (HR+/HER2-). The 70-gene signature (70-GS, MammaPrint) test is recommended for assessing recurrence risk and guiding adjuvant chemotherapy decisions for Luminal-type BC. This study updates previously established nomogram models to predict binary 70-GS risk for lymph node positive (LN+) and lymph node negative (LN-) luminal-type BC patients.

methodsThis retrospective study included 301 consecutive female patients with HR+/HER2- BC treated at Peking Union Medical College Hospital from November 2019 to December 2023. Patients' medical history, imaging reports, and clinicopathological features were reviewed. Forty risk parameters were compared between 70-GS high and low-risk patients among LN + and LN- groups. High risk stratification criterion in MonarchE and Natalee were compared between low and high 70-GS risk for the first time. Logistic regression was utilized to establish nomogram models predicting binary 70-GS risk for LN + and LN- patients. The models' prediction performance was evaluated utilizing accuracy, AUC of ROC curves, C-index, calibration curves, and decision curve analysis.

resultsSignificant differences were found between 70-GS high and low-risk patients in both LN + and LN- groups. Among LN + patients, parameters including childbirth number (p = 0.024), cardiovascular diseases (p = 0.037), US min. diameter of tumor (p = 0.034), Ki67 index (p < 0.001) and PR positivity (p = 0.007) were significant predictors. Among LN- patients, micro-calcifications (p = 0.011), PR positivity (p = 0.021), and Ki67 index (p < 0.001) were significant. The nomogram models showed high predictive accuracy, with AUC of 0.948 in the training set (C-index 0.948, 0.914-0.982, accuracy 0.907) and 0.923 in the testing set (C-index 0.923, 0.919-0.927, accuracy 0.828) for LN + patients and 0.917 in the training set (C-index 0917, 0.861-0.972, accuracy 0.870) and 0.917 in the testing sets (C-index 0917, 0.912-0.922, accuracy 0.808) among LN- patients. Calibration and decision curve analyses confirmed model reliability and clinical utility.

conclusionsThe updated nomogram models for predicting 70-GS risk in LN + and LN- luminal-type BC patients demonstrated improved prediction performance. The models facilitate individualized risk assessment and treatment decision-making, emphasizing tailored approaches based on lymph node status.

Indexed as

Biomarkers, TumorBreast NeoplasmsLymph NodesNomogramsAdultAgedAxillaChinaEast Asian PeopleErb-b2 Receptor Tyrosine KinasesFemaleHumansLymphatic MetastasisMiddle AgedRetrospective StudiesRisk AssessmentBiomarkers, TumorErb-b2 Receptor Tyrosine Kinases70-gene signature (MammaPrint)Breast cancerLymph node negativeLymph node positiveNomogramRisk prediction

Identifiers

PMID40597882
PMCPMC12211753

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