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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy

Author(s): Mohammed El Adoui*, Stylianos Drisis and Mohammed Benjelloun

Volume 19, Issue 5, 2023

Published on: 26 September, 2022

Page: [526 - 533] Pages: 8

DOI: 10.2174/1573405618666220803124426


Purpose: To reduce breast tumor size before surgery, Neoadjuvant Chemotherapy (NAC) is applied systematically to patients with local breast cancer. However, with the existing clinical protocols, it is not yet possible to have an early prediction of the effect of chemotherapy on a breast tumor. Predicting the response to chemotherapy could reduce toxicity and delay effective treatment. Computational analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) through Deep Convolution Neural Network (CNN) has proved a significant performance in classifying responders and no responder’s patients. This study intends to present a new explainable Deep Learning (DL) model predicting the breast cancer response to chemotherapy based on multiple MRI inputs.

Material and Methods: In this study, a cohort of 42 breast cancer patients who underwent chemotherapy was used to train and validate the proposed DL model. This dataset was provided by the Jules Bordet institute of radiology in Brussels, Belgium. 14 external subjects were used to validate the DL model to classify responding or non-responding patients on temporal DCE-MRI volumes. The model performance was assessed by the Area Under the receiver operating characteristic Curve (AUC), accuracy, and features map visualization according to pathological complete response (Ground truth).

Results: The developed deep learning architecture was able to predict the responding breast tumors to chemotherapy treatment in the external validation dataset with an AUC of 0.93 using parallel learning MRI images acquired at different moments. The visual results showed that the most important extracted features from non-responding tumors are in the peripheral and external tumor regions. The model proposed in this study is more efficient compared to those proposed in the literature.

Conclusion: Even with a limited training dataset size, the developed multi-input CNN model using DCE-MR images acquired before and following the first chemotherapy was able to predict responding and non-responding tumors with higher accuracy. Thanks to the visualization of the extracted characteristics by the DL model on the responding and non-responding tumors, the latter could be used henceforth in clinical analysis after its evaluation based on more extra data.

Keywords: Deep learning, CNN, explainable DL, MRI, breast cancer.

Graphical Abstract
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