Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs
Open Access
- 8 July 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 11 (1), 1-12
- https://doi.org/10.1038/s41598-021-93592-z
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.Funding Information
- No funding
This publication has 50 references indexed in Scilit:
- Early prediction of pathologic response to neoadjuvant therapy in breast cancer: Systematic review of the accuracy of MRIThe Breast, 2012
- Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIALRadiology, 2012
- Magnetic Resonance Imaging Response Monitoring of Breast Cancer During Neoadjuvant Chemotherapy: Relevance of Breast Cancer SubtypeJournal of Clinical Oncology, 2011
- Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imagingEuropean Radiology, 2010
- Impact of Progression During Neoadjuvant Chemotherapy on Surgical Management of Breast CancerAnnals of Surgical Oncology, 2010
- New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)European Journal of Cancer, 2009
- Longitudinal study of the assessment by MRI and diffusion‐weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapyNMR in Biomedicine, 2008
- Stability of feature selection algorithms: a study on high-dimensional spacesKnowledge and Information Systems, 2006
- Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRIJournal of Magnetic Resonance Imaging, 2006
- On a Test of Whether one of Two Random Variables is Stochastically Larger than the OtherThe Annals of Mathematical Statistics, 1947