Validation of a deep learning–based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting

These findings highlight the potential of AI-based imaging tools to support real-time treatment monitoring in breast cancer. The observed trend favoring Herceptin suggests that AI-generated risk scores may serve as non-invasive indicators of treatment efficacy. Broader validation across larger, more diverse cohorts is warranted to confirm these preliminary results and further develop AI-guided oncology workflows.

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Publication Details

Journal
Frontiers in Oncology
Published
2025-08-14
DOI
https://doi.org/10.3389/fonc.2025.1639474
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
Controls
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article

Validation of a deep learning–based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting

Kathryn K. Malherbe
1 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
1.36
article

Validation of a deep learning–based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting

Kathryn K. Malherbe
article en
1 citations

Abstract

These findings highlight the potential of AI-based imaging tools to support real-time treatment monitoring in breast cancer. The observed trend favoring Herceptin suggests that AI-generated risk scores may serve as non-invasive indicators of treatment efficacy. Broader validation across larger, more diverse cohorts is warranted to confirm these preliminary results and further develop AI-guided oncology workflows.

Frontiers in OncologyVol. 15
University of Pretoria (ZA)
Openalex Percentile: Top 16%
Radiomics and Machine Learning in Medical Imaging
1.36
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