Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models

The 6-mm peritumoral zone demonstrated optimal predictive value for NAC response, with the AI-driven combined intratumoral-peritumoral model achieving superior performance. This standardized ABVS-based radiomics approach enables early identification of potential NAC non-responders, facilitating timely therapeutic modifications.

Authors

Yung‐Chang Lin (ORCID: https://orcid.org/0000-0002-2084-5348)
Ruiping Ren
Meng Wu (ORCID: https://orcid.org/0000-0003-2111-2645)
Wanjun Chen (ORCID: https://orcid.org/0000-0002-4418-7650)
Jing Tang (ORCID: https://orcid.org/0000-0002-8961-138X)
Minfang Wang

Institutions

Ningbo University (CN)

Publication Details

Journal: Frontiers in Oncology
Published: 2025-05-14
DOI: https://doi.org/10.3389/fonc.2025.1586715
Citations: 1
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 1.36

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article

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models

Yung‐Chang Lin, Ruiping Ren, Meng Wu, Wanjun Chen et al.

May 14, 2025

1 citations

Frontiers in Oncology

Radiomics and Machine Learning in Medical Imaging

1.36

article

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models

Yung‐Chang Lin, Ruiping Ren, Meng Wu, Wanjun Chen, Jing Tang, Minfang Wang

May 14, 2025article en

1 citations

Abstract

The 6-mm peritumoral zone demonstrated optimal predictive value for NAC response, with the AI-driven combined intratumoral-peritumoral model achieving superior performance. This standardized ABVS-based radiomics approach enables early identification of potential NAC non-responders, facilitating timely therapeutic modifications.

Frontiers in OncologyVol. 15

Ningbo University (CN)

Openalex Percentile: Top 20%

Radiomics and Machine Learning in Medical Imaging

1.36

DOI Open Access PDF OpenAlex

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