ArticleCancers2021
Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2023Pooled it
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.The Kaohsiung journal of medical sciences · 2026Article
- Multi-parametric MRI habitat radiomics with interpretable machine learning for early prediction of axillary lymph node metastasis in triple-negative breast cancer.Frontiers in medicine · 2026Article
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Radiomics Combined with Transcriptomics Improves Prediction of Breast Cancer Recurrence, Molecular Subtype and Grade.Cancers · 2025Article
- Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.Scientific reports · 2025Article
- Review
- Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.Cancer medicine · 2024Article
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- Impact of real-life use of artificial intelligence as support for human reading in a population-based breast cancer screening program with mammography and tomosynthesis.European radiology · 2024Article
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.Scientific reports · 2023Article
- 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
- Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress.Frontiers in oncology · 2023Review
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- Article
- Lean Perspectives in an Organizational Change in a Scientific Direction of an Italian Research Institute: Experience of the Cancer Institute of Bari.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.
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