Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
Open accessAbstract
The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.
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Radiological data has been combined with other data types for predictive AI systems in various clinical disciplines like oncology or neurology.
“Radiological data has been combined with other data types for predictive AI systems in various clinical disciplines like oncology21–24 or neurology25,26.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [22]
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- Authors
- Joo, S., Ko, E. S., Kwon, S., Jeon, E., Jung, H., Kim, J.-Y., Chung, M. J., & Im, Y.-H.
- Affiliations
- Samsung (South Korea) (KR)
- Samsung Medical Center (KR)
- Corresponding author
- Im, Young-Hyuck
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-09-22
- Volume
- 11
- Issue
- 1
- Pages
- 18800-18800
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8458289
- OpenAlex
- W3199109277
- Cited by
- 137
- References
- 42
- Citations by year
- 2026: 20 · 2025: 41 · 2024: 39 · 2023: 28 · 2022: 9
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Samsung (SMX1210791)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- breast cancer, neoadjuvant chemotherapy, pathological complete response, multimodal deep learning, pre-treatment MRI, T2-weighted imaging, treatment response prediction
- MeSH terms
- Deep Learning, Adult, Breast, Breast Neoplasms, Female, Humans, Magnetic Resonance Imaging, Middle Aged, Retrospective Studies, ROC Curve, Treatment Outcome, Neoadjuvant Therapy
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