Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer
Abstract
Purpose The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound.Methods Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness.Results The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91-0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful.Conclusion A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.
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Deep learning can improve the prediction ability of NAC response prediction in breast cancer.
“Many researchers found that deep learning can improve the prediction ability of NAC response prediction in breast cancer (13, 22).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [22]
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Self-citation
- Authors
- Jiang, M., Li, C.-L., Luo, X.-M., Chuan, Z.-R., Lv, W.-Z., Li, X., Cui, X.-W., & Dietrich, C. F.
- Affiliations
- Tongji Hospital (CN)
- Kunming Medical University (CN)
- South Central Minzu University (CN)
- Corresponding author
- Cui, Xin-Wu
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 147
- Pages
- 95-105
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W3132834592
- Cited by
- 214
- References
- 31
- Citations by year
- 2026: 20 · 2025: 51 · 2024: 59 · 2023: 53 · 2022: 29 · 2021: 2
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Cancer (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- locally advanced breast cancer, neoadjuvant chemotherapy, pathological complete response, deep learning radiomics, radiomics nomogram, breast ultrasound, preoperative prediction, triple-negative breast cancer, HER2-positive breast cancer, hormone receptor-positive breast cancer, treatment response assessment
- MeSH terms
- Clinical Decision-Making, Deep Learning, Adult, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Mastectomy, Middle Aged, Neoplasm Invasiveness, Predictive Value of Tests, Retrospective Studies, Reproducibility of Results, Observer Variation, Ultrasonography, Mammary, Treatment Outcome, Chemotherapy, Adjuvant, Neoadjuvant Therapy, Nomograms
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