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用于预测HER2阴性乳腺癌患者HER2低表达的瘤内和瘤周超声影像组学分析:一项双中心研究的回顾性分析

Intratumoral and peritumoral ultrasound radiomics analysis for predicting HER2-low expression in HER2-negative breast cancer patients: a retrospective analysis of dual-central study.

作者信息

Wang Jiajia, Gu Yunxin, Zhan Yunyun, Li Rubing, Bi Yu, Gao Lan, Wu Xiabi, Shao Jiaqi, Chen Yilin, Ye Lei, Peng Mei

机构信息

Department of Ultrasound Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.

Department of Clinical Medicine, The Second School of Clinical Medicine, Anhui Medical University, Hefei, 230601, Anhui, China.

出版信息

Discov Oncol. 2025 Jun 5;16(1):1007. doi: 10.1007/s12672-025-02752-4.


DOI:10.1007/s12672-025-02752-4
PMID:40471472
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12141173/
Abstract

OBJECTIVE: This study aims to explore whether intratumoral and peritumoral ultrasound radiomics of ultrasound images can predict the low expression status of human epidermal growth factor receptor 2 (HER2) in HER2-negative breast cancer patients. METHODS: HER2-negative breast cancer patients were recruited retrospectively and randomly divided into a training cohort (n = 303) and a test cohort (n = 130) at a ratio of 7:3. The region of interest within the breast ultrasound image was designated as the intratumoral region, and expansions of 3 mm, 5 mm, and 8 mm from this region were considered as the peritumoral regions for the extraction of ultrasound radiomic features. Feature extraction and selection were performed, and radiomics scores (Rad-score) were obtained in four ultrasound radiomics scenarios: intratumoral only, intratumoral + peritumoral 3 mm, intratumoral + peritumoral 5 mm, and intratumoral + peritumoral 8 mm. An optimal combined nomogram radiomic model incorporating clinical features was established and validated. Subsequently, the diagnostic performance of the radiomic models was evaluated. RESULTS: The results indicated that the intratumoral + peritumoral (5 mm) ultrasound radiomics exhibited the excellent diagnostic performance in evaluated the HER2 low expression. The nomogram combining intratumoral + peritumoral (5 mm) and clinical features showed superior diagnostic performance, achieving an area under the curve (AUC) of 0.911 and 0.869 in the training and test cohorts, respectively. CONCLUSION: The combination of intratumoral + peritumoral (5 mm) ultrasound radiomics and clinical features possesses the capability to accurately predict the low-expression status of HER2 in HER2-negative breast cancer patients.

摘要

目的:本研究旨在探讨超声图像的肿瘤内及瘤周超声影像组学能否预测人表皮生长因子受体2(HER2)阴性乳腺癌患者中HER2的低表达状态。 方法:回顾性招募HER2阴性乳腺癌患者,并按7:3的比例随机分为训练队列(n = 303)和测试队列(n = 130)。将乳腺超声图像中的感兴趣区域指定为肿瘤内区域,从该区域向外扩展3 mm、5 mm和8 mm作为瘤周区域,用于提取超声影像组学特征。进行特征提取和选择,并在四种超声影像组学情况下获得影像组学评分(Rad-score):仅肿瘤内、肿瘤内+瘤周3 mm、肿瘤内+瘤周5 mm和肿瘤内+瘤周8 mm。建立并验证了一个结合临床特征的最佳联合列线图影像组学模型。随后,评估影像组学模型的诊断性能。 结果:结果表明,肿瘤内+瘤周(5 mm)超声影像组学在评估HER2低表达方面表现出优异的诊断性能。结合肿瘤内+瘤周(5 mm)和临床特征的列线图显示出卓越的诊断性能,在训练队列和测试队列中的曲线下面积(AUC)分别达到0.911和0.869。 结论:肿瘤内+瘤周(5 mm)超声影像组学与临床特征相结合,有能力准确预测HER2阴性乳腺癌患者中HER2的低表达状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/4b211ee9e18e/12672_2025_2752_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/6d60dac7fe58/12672_2025_2752_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/6a1f8b7e6fb9/12672_2025_2752_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/30ea0ea59017/12672_2025_2752_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/1a6d28389308/12672_2025_2752_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/44a80d2a8d83/12672_2025_2752_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/4b211ee9e18e/12672_2025_2752_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/6d60dac7fe58/12672_2025_2752_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/6a1f8b7e6fb9/12672_2025_2752_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/30ea0ea59017/12672_2025_2752_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/1a6d28389308/12672_2025_2752_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/44a80d2a8d83/12672_2025_2752_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe7/12141173/4b211ee9e18e/12672_2025_2752_Fig6_HTML.jpg

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本文引用的文献

[1]
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[2]
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Cancer Med. 2024-9

[3]
Preliminary study on DCE-MRI radiomics analysis for differentiation of HER2-low and HER2-zero breast cancer.

Front Oncol. 2024-8-15

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Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach.

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Cancer Cell Int. 2024-7-27

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[8]
Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.

Heliyon. 2024-4-4

[9]
Ultrasound-based radiomics-clinical nomogram for noninvasive prediction of residual cancer burden grading in breast cancer.

J Clin Ultrasound. 2024-6

[10]
The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer.

Acad Radiol. 2024-7

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