Evidence map›Paper›PMID 40358626›Full record

Observational studyInternational journal of surgery (London, England)2025

The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: a multicenter, prospective, observational study.

Jia-Xin Huang, Jing-Si Mei, Fei Chen, Jia-Hui Huang, Yu-Ting Tan, Yi-Wen Wu, Feng-Tao Liu, Shao-Dong Qiu, Cai-Gou Shi, Yao Lu and 5 more

Abstract readObservational StudyMulticenter StudyValidation Study
In one paragraph

Observational study in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

15 authors.

Jia-Xin HuangDepartment of Liver Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Jing-Si MeiBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Fei ChenDepartment of Medical Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jia-Hui HuangInstitute of Artificial Intelligence, Guangzhou University, Guangzhou, China.
Yu-Ting TanBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Yi-Wen WuDepartment of Medical Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Feng-Tao LiuBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Shao-Dong QiuDepartment of Medical Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Cai-Gou ShiDepartment of Medical Ultrasound, Liuzhou People's Hospital, Liuzhou, China.
Yao LuSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Xue-Yan WangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Gui-Ling HuangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Yu-Ting ZhangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Min-Shan ChenDepartment of Liver Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Xiao-Qinsg PeiDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIt is not clear which procedure is most optimal for axilla after neoadjuvant therapy (NAT) in node-positive breast cancer patients. Accurately identifying patients with axillary pathologic complete response (pCR) is crucial to minimize the overtreatment of axilla. This study was designed to develop a risk stratification model for axillary pCR.

methodsIn this multicenter, prospective, observational study, node-positive breast cancer patients who received NAT followed by axillary lymph node dissection (ALND) were enrolled between June 2021 and April 2024. We assessed the performance of breast shear wave elastography (SWE) utilizing virtual touch imaging quantification in determining axillary status across ultrasound (US) nodal stages following NAT. A predictive model incorporating axilla US nodal stage and breast SWE was developed using multivariate logistic regression analysis. Last, a simplified risk score was developed based on the calculated prediction probability from this model and validated in the external test cohort.

resultsThe axillary pCR rates were 52.53% in the training cohort ( n = 257) and 51.79% in the external test cohorts ( n = 195). Approximately 21.67% of US N0 cases were false negatives; 42.35% of US N1 cases were false positives. With SWE, the false negative rate was 11.53% in US N0 patients and false positive rate was 22.22% in US N1 patients. The model based on dual-modality US demonstrated strong discriminatory ability (AUC, 0.93), precise calibration (slope of calibration curve, 0.99), and practical clinical utility (probability threshold, 4.5-94.5%); the percentages of accuracy, sensitivity, and specificity were 87.94%, 88.52%, and 87.41%, respectively. Patients scoring 1 demonstrated a low axillary non-pCR rate (5.21%-6.97%), potentially reducing unnecessary ALND rate (17.12%-24.10%).

conclusionsThe risk stratification model integrating axilla US and breast SWE demonstrated good performance for assessing axillary status after NAT in node-positive breast cancer and might provide guidance for less aggressive management for specific individuals.

Indexed as

Breast NeoplasmsLymph NodesNeoadjuvant TherapyAdultAgedAxillaElasticity Imaging TechniquesFemaleHumansLymphatic MetastasisLymph Node ExcisionMiddle AgedProspective StudiesRisk Assessmentbreast neoplasmelasticity imaging techniqueslymph nodeneoadjuvant therapy

Identifiers

PMID40358626
PMCPMC12165591

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.