Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach.
The objective is to evaluate the feasibility of utilizing ultrasound images in identifying critical prognostic biomarkers for HER2-positive breast cancer (HER2 + BC). This study enrolled 512 female patients diagnosed with HER2-positive breast cancer through pathological validation at our institution...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 850 - 858 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081735 184081735 184081735 10.1007/s10278-024-01229-0 184081735 ppf: 850 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach. aug: au: Chen, Zhi-hui Zha, Hai-ling Yao, Qing Zhang, Wen-bo Zhou, Guang-quan Li, Cui-ying affil: https://ror.org/05hfa4n20 Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng district, 310006, Hangzhou, China sug: subj: HER-2-neu Oncogene Breast Neoplasms Ultrasonography Image Processing, Computer Assisted Predictive Value of Tests Deep Learning Breast Neoplasms Prognosis Human Female Adult Middle Age Aged Retrospective Design Record Review Convolutional Neural Networks Axilla Ultrasonography Lymph Nodes Ultrasonography Neoplasm Invasiveness Neoplasm Grading Validity Sensitivity and Specificity ROC Curve Descriptive Statistics Confidence Intervals Data Analysis Software Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female ab: The objective is to evaluate the feasibility of utilizing ultrasound images in identifying critical prognostic biomarkers for HER2-positive breast cancer (HER2 + BC). This study enrolled 512 female patients diagnosed with HER2-positive breast cancer through pathological validation at our institution from January 2016 to December 2021. Five distinct deep convolutional neural networks (DCNNs) and a deep ensemble (DE) approach were trained to classify axillary lymph node involvement (ALNM), lymphovascular invasion (LVI), and histological grade (HG). The efficacy of the models was evaluated based on accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curves, areas under the ROC curve (AUCs), and heat maps. DeLong test was applied to compare differences in AUC among different models. The deep ensemble approach, as the most effective model, demonstrated AUCs and accuracy of 0.869 (95% CI: 0.802–0.936) and 69.7% in LVI, 0.973 (95% CI: 0.949–0.998) and 73.8% in HG, thus providing superior classification performance in the context of imbalanced data (p < 0.05 by the DeLong test). On ALNM, AUC and accuracy were 0.780 (95% CI: 0.688–0.873) and 77.5%, which were comparable to other single models. The pretreatment US-based DE model could hold promise as a clinical guidance for predicting pathological characteristics of patients with HER2-positive breast cancer, thereby providing benefit of facilitating timely adjustments in treatment strategies. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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