Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis

Sureness metrics can provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion is invasive.

Authors

Alexandra Edwards
Karen Drukker (ORCID: https://orcid.org/0000-0001-6544-3476)
Maryellen L. Giger (ORCID: https://orcid.org/0000-0001-5482-9728)
Heather M. Whitney (ORCID: https://orcid.org/0000-0001-6450-8266)

Institutions

University of Chicago (US)

Publication Details

Journal: Journal of Medical Imaging
Published: 2025-06-18
DOI: https://doi.org/10.1117/1.jmi.12.s2.s22012
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Institute of Biomedical Imaging and Bioengineering

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article

Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis

Alexandra Edwards, Karen Drukker, Maryellen L. Giger, Heather M. Whitney

Jun 18, 2025

Journal of Medical Imaging

Radiomics and Machine Learning in Medical Imaging

article

Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis

Alexandra Edwards, Karen Drukker, Maryellen L. Giger, Heather M. Whitney

June 18, 2025article en

Abstract

Sureness metrics can provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion is invasive.

Journal of Medical ImagingVol. 12(S2)

University of Chicago (US)

National Institute of Biomedical Imaging and Bioengineering

Good health and well-being

Openalex Percentile: Top 79%

Radiomics and Machine Learning in Medical Imaging

DOI Source OpenAlex

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