Evidence map›Paper›PMID 41269377›Full record

SynthesisBreast cancer research and treatment2025

Risk prediction models for malignancy upgrade in high-risk breast lesions: a qualitative systematic review.

Juliet C Dalton, Tori C Nierenberg, Austin Leonard, Joey Liang, Samantha Kaplan, Ton Wang, Akiko Chiba, Jennifer K Plichta

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis 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.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Juliet C DaltonDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Tori C NierenbergDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Austin LeonardDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Joey LiangDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Samantha KaplanMedical Center Library, Duke University, Durham, NC, 27710, USA.
Ton WangDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Akiko ChibaDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA.
Jennifer K PlichtaDepartment of Surgery, Duke University Medical Center, DUMC 3513, Durham, NC, 27710, USA. Jennifer.Plichta@duke.edu.ORCID http://orcid.org/0000-0002-7411-0558

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAtypical breast lesions are high-risk findings found on some core needle biopsies that may represent concurrent malignancy. Clinical management remains uncertain due to wide variability in reported upgrade rates and an incomplete understanding of contributing risk factors. Risk prediction models have been developed to estimate likelihood of malignant upgrade (from atypia to malignancy), but these models are highly variable in performance and predictor selection. This systematic review evaluates existing models predicting upgrade to malignancy in high-risk breast lesions, focusing on clinical applicability.

methodsA qualitative systematic review was conducted following PRISMA guidelines. Searches in MEDLINE, Embase, and Scopus identified studies that developed risk prediction models estimating breast malignancy upgrade after atypia diagnosis. Studies analyzing multiple risk factors and providing quantitative risk estimates were included. Extracted data included study characteristics, statistical methods, key predictors, and model performance. Prediction model Risk of Bias Assessment Tool (PROBAST) was used for quality assessment.

resultsOf the 3202 studies screened, 17 met inclusion criteria. Sample sizes ranged from 20 to 525, with reported upgrade rates from 14.9 to 67.3%. Common predictors of upgrade included lesion size, histology, and radiologic-pathologic concordance. Discriminatory performance varied (AUROC 0.514-0.909), and calibration was rarely assessed, limiting reliability. Most studies lacked external validation and exhibited a high risk of bias.

conclusionCurrent risk prediction models for malignant upgrade for high-risk lesions demonstrate significant variability and limitations in widespread use. While they may supplement clinical judgment, further external validation and improved calibration are needed before they can reliably guide management.

Indexed as

Breast NeoplasmsModels, StatisticalBiopsy, Large-Core NeedleBreastFemaleHumansNeoplasm GradingRisk AssessmentRisk FactorsAtypiaBreast cancerRisk assessmentSystematic reviewTool

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