Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study.

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Siwei Luo, Xiaobo Chen, Mengxia Yao, Yuanlin Ying, Zena Huang, Xiaoya Zhou, Zuwei Liao, Lijie Zhang, Na Hu, Chunwang Huang
{"title":"Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study.","authors":"Siwei Luo, Xiaobo Chen, Mengxia Yao, Yuanlin Ying, Zena Huang, Xiaoya Zhou, Zuwei Liao, Lijie Zhang, Na Hu, Chunwang Huang","doi":"10.1186/s13244-025-01934-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively.</p><p><strong>Methods: </strong>Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction.</p><p><strong>Results: </strong>The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test cohorts, the patient-level combined model showed the best discrimination ability with the macro-AUCs of 0.988 (95% CI: 0.983-0.992), 0.915 (95% CI: 0.851-0.965), and 0.862 (95% CI: 0.820-0.899), respectively.</p><p><strong>Conclusion: </strong>This study built a robust and interpretable clinical-radiomics model to evaluate three classes of HER2 status based on ultrasound images.</p><p><strong>Critical relevance statement: </strong>Ultrasound-based radiomics method can noninvasively identify three different states of HER2, which may guide treatment decisions and the implementation of personalized HER2-targeted treatment for invasive breast cancer patients.</p><p><strong>Key points: </strong>Determination of HER2 status can affect treatment options for breast cancer. The ultrasound-based clinical-radiomics model can discriminate the three different HER2 statuses. Our developed model can assist in providing personalized recommendations for novel HER2-targeted therapies.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"53"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insights into Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13244-025-01934-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Objectives: Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively.

Methods: Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction.

Results: The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test cohorts, the patient-level combined model showed the best discrimination ability with the macro-AUCs of 0.988 (95% CI: 0.983-0.992), 0.915 (95% CI: 0.851-0.965), and 0.862 (95% CI: 0.820-0.899), respectively.

Conclusion: This study built a robust and interpretable clinical-radiomics model to evaluate three classes of HER2 status based on ultrasound images.

Critical relevance statement: Ultrasound-based radiomics method can noninvasively identify three different states of HER2, which may guide treatment decisions and the implementation of personalized HER2-targeted treatment for invasive breast cancer patients.

Key points: Determination of HER2 status can affect treatment options for breast cancer. The ultrasound-based clinical-radiomics model can discriminate the three different HER2 statuses. Our developed model can assist in providing personalized recommendations for novel HER2-targeted therapies.

求助全文
约1分钟内获得全文 求助全文
来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信