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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889314/pdf/","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.

基于超声的肿瘤内和肿瘤周围放射组学术前预测低her2乳腺癌:一项多中心回顾性研究。
目的:人类表皮生长因子受体2 (HER2)靶向治疗的最新进展为HER2低水平癌症开辟了新的治疗选择。本研究旨在建立一种基于超声的放射组学模型,以无创地识别三种不同的HER2状态。方法:2018年5月至2023年12月,从三家医院共纳入1257例浸润性乳腺癌患者。HER2状态分为阳性、低、零三类。通过将人工分割的肿瘤内感兴趣区域(ROI)扩大到5mm、10mm、15mm和20mm的厚度,自动生成四个肿瘤周围感兴趣区域(ROI)。图像预处理后,从每位患者的每张图像中提取4720个放射组学特征。利用最小绝对收缩算子和选择算子以及LightBoost算法构建单区域和多区域放射组学特征(RS)。通过将临床-超声鉴别因素与最佳RS相结合,建立临床-放射组学联合模型,并采用数据拼接策略构建患者级模型。采用Shapley加性解释(SHAP)方法来解释内部预测的贡献。结果:综合12个肿瘤特征和9个瘤周-15mm特征构建了最优RS。保留年龄、肿瘤大小和7个定性超声特征,构建具有最佳RS的临床-放射组学联合模型。在训练、验证和检验队列中,患者水平联合模型的宏观auc分别为0.988 (95% CI: 0.983-0.992)、0.915 (95% CI: 0.851-0.965)和0.862 (95% CI: 0.820-0.899),表现出最佳的鉴别能力。结论:本研究建立了一个稳健且可解释的临床放射组学模型,用于基于超声图像评估三类HER2状态。关键相关性声明:基于超声的放射组学方法可以无创地识别HER2的三种不同状态,可以指导浸润性乳腺癌患者的治疗决策和实施个性化的HER2靶向治疗。重点:HER2状态的测定可以影响乳腺癌的治疗选择。基于超声的临床放射组学模型可以区分三种不同的HER2状态。我们开发的模型可以帮助为新的her2靶向治疗提供个性化建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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