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Cited by 126Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisBreast Cancer Treatment Studies

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study

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Gu, Jionghui, Tong, Tong, He, Chang, Xu, Min, Yang, Xin, Tian, Jie, Jiang, Tianan, Wang, Kun·2022·European Radiology
DOI 10.1007/s00330-021-08293-y
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Abstract

Objectives Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.Methods In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration.Results In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC.Conclusions The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients.Key points • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.

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Metadata

Authors
Gu, J., Tong, T., He, C., Xu, M., Yang, X., Tian, J., Jiang, T., & Wang, K.
Affiliations
  • Chinese Academy of Sciences (CN)
  • First Affiliated Hospital Zhejiang University (CN)
Corresponding author
  • Jiang, Tianan
  • Wang, Kun
ORCID
Venue
European Radiology
Publisher
Springer Science and Business Media LLC
Published
2021-10-15
Volume
32
Issue
3
Pages
2099-2109
ISSN
  • 0938-7994 (ISSN-L)
  • 1432-1084
DOI
10.1007/s00330-021-08293-y
OpenAlex
W3206781867
Cited by
126
References
37
Citations by year
2026: 10 · 2025: 27 · 2024: 28 · 2023: 41 · 2022: 17 · 2021: 2
FWCI
11.60
Type
journal article
Language
en
Open access
Licence
Locations
  • European Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • European Society of Radiology
Funders
  • Ministry of Science and Technology (2017YFA0205200)
  • Key Technologies Research and Development Program (2018YFC0114900)
  • National Natural Science Foundation of China (62027901, 81930053, 81227901, 82027803, 81971623)
  • Chinese Academy of Sciences (YJKYYQ20180048, QYZDJ-SSW-JSC005)
  • Youth Innovation Promotion Association CAS
  • Project of High-Level Talents Team Introduction in Zhuhai City
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
breast cancer, neoadjuvant chemotherapy, treatment response prediction, deep learning radiomics, breast ultrasound, early treatment response prediction, prognostic imaging biomarkers, treatment cost reduction, treatment time reduction
MeSH terms
Deep Learning, Breast Neoplasms, Female, Humans, Prospective Studies, Retrospective Studies, Ultrasonography, Neoadjuvant Therapy

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