Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study Article Swipe
Ying Fu
,
Yutao Lei
,
Yühong Huang
,
Mei Fang
,
Song Wang
,
Kun Yan
,
Yihua Wang
,
Yihan Ma
,
Ligang Cui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s00330-024-10786-5
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s00330-024-10786-5
Objectives Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer’s axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. Methods Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model’s performance against sonographer’s diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. Results In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer’s diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer’s diagnostic ability, increasing accuracy from 71.9% to 79.2%. Conclusion The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. Clinical relevance statement Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer’s axillary diagnosis. Key Points Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound . Our AI model outperformed sonographer’s visual diagnosis on axillary ultrasound . Our deep learning radiomics model can improve sonographers’ diagnosis and might assist in surgical decision-making .
Related Topics
Concepts
Sonographer
Medicine
Breast cancer
Radiology
Ultrasound
Lymph node
Echogenicity
Sentinel lymph node
Internal medicine
Cancer
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00330-024-10786-5
- https://link.springer.com/content/pdf/10.1007/s00330-024-10786-5.pdf
- OA Status
- hybrid
- Cited By
- 17
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396811340
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396811340Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00330-024-10786-5Digital Object Identifier
- Title
-
Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-10Full publication date if available
- Authors
-
Ying Fu, Yutao Lei, Yühong Huang, Mei Fang, Song Wang, Kun Yan, Yihua Wang, Yihan Ma, Ligang CuiList of authors in order
- Landing page
-
https://doi.org/10.1007/s00330-024-10786-5Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s00330-024-10786-5.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s00330-024-10786-5.pdfDirect OA link when available
- Concepts
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Sonographer, Medicine, Breast cancer, Radiology, Ultrasound, Lymph node, Echogenicity, Sentinel lymph node, Internal medicine, CancerTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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