Evidence map›Paper›PMID 40740870›Full record

ArticleFrontiers in oncology2025

Radiotherapy improves survival in HER2-positive breast cancer with lung metastases: a retrospective study with artificial intelligence-based prognostic modeling.

Tengfei Ren, Xiangkun Wang, Shengyuan Luo, Shanbao Ke

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

4 authors.

Tengfei RenDepartment of Hemangioma, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, China.
Xiangkun WangDepartment of Breast and Thyroid Surgery, Juye County People's Hospital, Heze, Shandong, China.
Shengyuan LuoDepartment of The Second Clinical Medical College, Changzhi Medical College, Changzhi, China.
Shanbao KeDepartment of Oncology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Human epidermal growth factor receptor 2 (HER2)-positive breast cancer is an aggressive subtype with a high risk of distant metastasis, particularly to the lungs. While systemic therapies have improved outcomes, the role of radiotherapy (RT) in the management of lung metastases remains uncertain. Methods: This retrospective study analyzed 248 HER2-positive breast cancer patients with lung metastases treated at two institutions between 2006 and 2021. Propensity score matching (PSM) was used to balance baseline characteristics between the RT and non-RT groups. Overall survival (OS) was assessed using Kaplan-Meier curves and Cox regression. A least absolute shrinkage and selection operator (LASSO)-Cox model was developed to identify prognostic factors, and its performance was evaluated using risk score visualization, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Results: RT significantly improved median OS both before (50.4 vs. 34.0 months, p < 0.001) and after PSM (51.5 vs. 32.3 months, p < 0.001). LASSO-Cox analysis confirmed RT as an independent prognostic factor. The predictive model demonstrated good discrimination (1- and 3-year AUCs of 0.716 and 0.722, respectively) and clinical utility by DCA. Conclusion: RT offers a significant survival benefit in HER2-positive breast cancer patients with lung metastases. AI-based modeling enhances prognostic accuracy and supports personalized treatment decisions.

Indexed as

artificial intelligencebreast cancerlung metastasesradiotherapysurvival

Identifiers

PMID40740870
PMCPMC12307436

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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.