Evidence map›Paper›PMID 42539395›Full record

ArticleFrontiers in oncology2026

A machine learning-based prognostic model for

Sihang Lin, Wanwan Wang, Lixia Liu, Jiayu Guan, Chuanrong Cen, Huawei Yang

Abstract read
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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Sihang Lin *Department of Breast Surgery, Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Guangxi Medical University Cancer Hospital, Nanning, China.
Wanwan Wang *Department of Breast Surgery, Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Guangxi Medical University Cancer Hospital, Nanning, China.
Lixia LiuDepartment of Breast Surgery, Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Guangxi Medical University Cancer Hospital, Nanning, China.
Jiayu GuanThe Second Clinical Medical College of Jinan University, Shenzhen, China.
Chuanrong CenDepartment of Breast Surgery, Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Guangxi Medical University Cancer Hospital, Nanning, China.
Huawei YangDepartment of Breast Surgery, Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Guangxi Medical University Cancer Hospital, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the prognostic value of postoperative radiotherapy (RT) for overall survival (OS) in patients with Methods: 2,266 patients with Results: In the training cohort, the RT group demonstrated significantly superior OS compared with the non-RT group (HR = 0.52, P < 0.001). Multivariate analysis confirmed RT as an independent protective factor for OS (HR = 0.657, P < 0.001), a finding further validated in the external validation cohort (HR = 0.171, P = 0.015). Among the four models, the LR model achieved the best performance, with an AUC of 0.721, an AP of 0.429, and a calibration slope of 0.97. DCA demonstrated favorable net clinical benefit for the LR model. Conclusion: Postoperative RT is an independent protective factor for OS in patients with

Indexed as

hormone receptor-positivemachine learningmetastatic breast cancerpostoperative radiotherapyprognostic prediction modelSEER database

Identifiers

PMID42539395
PMCPMC13423610

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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.