Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer

The GNB model demonstrated relatively superior and stable predictive performance in internal testing, suggesting that DLCT radiomics may offer a potential auxiliary tool for distinguishing between Luminal and non-Luminal BC. However, further validation through large-scale multicenter studies is required.

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Publication Details

Journal
Frontiers in Oncology
Published
2026-02-05
DOI
https://doi.org/10.3389/fonc.2026.1739346
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer

Yikun Ma, Bo Shi, Zhijing Song, Zhiyang Dou
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer

Yikun Ma, Bo Shi, Zhijing Song, Zhiyang Dou
article en

Abstract

The GNB model demonstrated relatively superior and stable predictive performance in internal testing, suggesting that DLCT radiomics may offer a potential auxiliary tool for distinguishing between Luminal and non-Luminal BC. However, further validation through large-scale multicenter studies is required.

Frontiers in OncologyVol. 16
Bengbu Medical College (CN), Anhui Medical University (CN), Jiangsu Cancer Hospital (CN), Nanjing Medical University (CN)
Openalex Percentile: Top 66%
Radiomics and Machine Learning in Medical Imaging
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Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer — Yikun Ma, Bo Shi, et al. · Frontiers in Oncology (2026) | TGRS Research Map | TGRS