Evidence map›Paper›PMID 42300305›Full record

ArticleCurrent medical imaging2026

Creation and Verification of a Machine Learning-based Model for Predicting Breast Cancer Risk in Patients with BI-RADS 4 Breast Lesions.

Jiahua Zhao, Bo Liang, Jiaxin Yan, Jianting Zheng, Shurong Chen, Haiqing Chen, Jun Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current medical imaging, 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

7 authors.

Jiahua ZhaoDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Bo LiangDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Jiaxin YanDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Jianting ZhengDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Shurong ChenDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Haiqing ChenDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.
Jun ZhouDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo develop and validate a machine learning-based model for predicting the risk of breast cancer occurrence in BI-RADS 4 patients.

methods216 breast lesions from 212 patients were included for retrospective analysis. The training set (151 cases) and the validation set (65 cases) were randomly divided from the whole data set at a ratio of 7:3. Use logistic as well as LASSO regressions to identify independent risk factors. Subsequently, eight ML models were constructed. After a comprehensive comparison, the optimal model was selected and visualized using the SHAP algorithm.

resultsThrough feature selection, six parameters were identified as independent risk factors. The Emax-2 shell cutoff (104.71 kPa) demonstrated the highest diagnostic efficacy for the 4a subgroup. Among the eight ML algorithms, the random forest model exhibited potential overfitting risks(AUC=1.00), whereas the LR model demonstrated superior stability. Consequently, the LR model was selected as the predictive model, and a nomogram was constructed based on it. DISCUSSION: In this study, the LR model enhanced the capacity to identify BI-RADS 4 lesions as BC. However, the study is a single-center study with a relatively small sample size, which brings certain restrictions to the clinical application of the model.

conclusionThe LR model is the best choice for predicting BC incidence in BI-RADS 4 lesions, which can help clinicians improve BC identification and treatment at an early stage.

Indexed as

Breast NeoplasmsMachine LearningUltrasonography, MammaryAdultAgedAlgorithmsClassification AlgorithmsFemaleHumansMiddle AgedNomogramsPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective StudiesRisk AssessmentBreast imaging reporting and data system categoryBreast lesionsElasticity imaging techniquesMachine learningNomogramShapley additive explanation

Identifiers

PMID42300305
PMCPMC13602108

What OpenQuestion holds

Textmetadata
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.