ArticleScientific reports2021
Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 4 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Pooled 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
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Ultrasound and Clinicopathological Features-Based Machine Learning Model for Predicting Neoadjuvant Therapy Efficacy in Breast Cancer.Cancer reports (Hoboken, N.J.) · 2026Article
- Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer.BMC cancer · 2025Article
- Article
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.Cancer medicine · 2024Article
- An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.Nature communications · 2024Article
- Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer.Scientific reports · 2024Article
- An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.Journal of translational medicine · 2024Article
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 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
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.
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Identifiers
34238968PMC8266861W3177894684What OpenQuestion holds
Registered trials
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.