ArticleInternational journal of computer assisted radiology and surgery2020
Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Pooled it
- Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Longitudinal MRI-based deep learning model for predicting pathological complete response in breast cancer: a multicenter, retrospective cohort study.NPJ precision oncology · 2026Article
- [Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI.Scientific reports · 2025Article
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.BMC medical imaging · 2025Article
- Complete end-to-end learning from protein feature representation to protein interactome inference.GigaScience · 2025Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.Nature communications · 2024Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.European radiology · 2024Article
- Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.Quantitative imaging in medicine and surgery · 2024Article
- Review
- Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records.Translational lung cancer research · 2023Article
- Article
- Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
purposeNeoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder's patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs.
methodsA cohort of 723 axial slices extracted from 42 breast cancer patients who underwent NAC therapy was used to train and validate the developed DL model. This dataset was provided by our collaborator institute of radiology in Brussels. Fourteen external cases were used to validate the best obtained model to predict pCR based on pre- and post-chemotherapy DCE-MRI. The model performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and feature map visualization.
resultsThe developed multi-inputs deep learning architecture was able to predict the pCR to NAC treatment in the validation dataset with an AUC of 0.91 using combined pre- and post-NAC images. The visual results showed that the most important extracted features from non-pCR tumors are in the peripheral region. The proposed method was more productive than the previous ones.
conclusionEven with a limited training dataset size, the proposed and developed CNN model using DCE-MR images acquired before and after the first chemotherapy was able to classify pCR and non-pCR patients with substantial accuracy. This model could be used hereafter in clinical analysis after its evaluation based on more extra data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.