Evidence map›Paper›PMID 39002069›Full record

ArticleBreast cancer research and treatment2025

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

I P C Buzatto, S A Recife, L Miguel, R M Bonini, N Onari, A L P A Faim, L Silvestre, D P Carlotti, A Fröhlich, D G Tiezzi

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Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

I P C BuzattoDepartment of Obstetrics and Gynecology - Breast Disease Division, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
S A RecifeDepartment of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
L MiguelDepartment of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
R M BoniniDepartment of Radiology, Hospital de Amor de Campo Grande, Campo Grande, Mato Grosso Do Sul, Brazil.
N OnariDepartment of Radiology, Hospital de Amor de Barretos, Barretos, Brazil.
A L P A FaimDepartment of Radiology, Hospital de Amor de Barretos, Barretos, Brazil.
L SilvestreDepartment of Obstetrics and Gynecology - Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
D P CarlottiInstitute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
A FröhlichDepartment of Mathematics, Federal University of Santa Catarina, Florianópolis, Brazil.
D G TiezziDepartment of Obstetrics and Gynecology - Breast Disease Division and Laboratory for Translational Data Science, Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3.900, Monte Alegre, Ribeirão Preto, Ribeirão Preto, Brazil. dtiezzi@usp.br.ORCID http://orcid.org/0000-0002-2660-0093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.

methodsWe included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset). We tested the performance of the final model with 403 new suspicious lesions.

resultsThe most informative attributes were shape, margin, orientation and size of the lesions, the resistance index of the internal vessel, the age of the patient and the presence of a palpable lump. The highest mean negative predictive value (NPV) was achieved with the K-Nearest Neighbors algorithm (97.9%). Making ensembles did not improve the performance. Tuning the threshold did improve the performance of the models and we chose the algorithm XGBoost with the tuned threshold as the final one. The tested performance of the final model was: NPV 98.1%, false negative 1.9%, positive predictive value 77.1%, false positive 22.9%. Applying this final model, we would have missed 2 of the 231 malignant lesions of the test dataset (0.8%).

conclusionMachine learning can help physicians predict malignancy in suspicious breast lesions identified by the US. Our final model would be able to avoid 60.4% of the biopsies in benign lesions missing less than 1% of the cancer cases.

Indexed as

Breast NeoplasmsMachine LearningUltrasonography, MammaryAdultAgedAlgorithmsBreastFemaleHumansMiddle AgedPredictive Value of TestsArtificial intelligenceBreast biopsyBreast ultrasoundMachine learningPrediction

Identifiers

PMID39002069

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