2 research outputs found
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Predicting response to neoadjuvant therapy is a vexing challenge in breast
cancer. In this study, we evaluate the ability of deep learning to predict
response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment
dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a
retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast
cancer patients from 5 institutions, we developed and validated a deep learning
approach for predicting pathological complete response (pCR) to HER2-targeted
NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant
chemotherapy at a single institution were used to train (n=85) and tune (n=15)
a convolutional neural network (CNN) to predict pCR. A multi-input CNN
leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was
identified to achieve optimal response prediction within the validation set
(AUC=0.93). This model was then tested on two independent testing cohorts with
pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient
testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and
a 29 patient multicenter trial including data from 3 additional institutions
(AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction
model was found to exceed a multivariable model incorporating predictive
clinical variables (AUC < .65 in testing cohorts) and a model of
semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing
cohorts). The results presented in this work across multiple sites suggest that
with further validation deep learning could provide an effective and reliable
tool to guide targeted therapy in breast cancer, thus reducing overtreatment
among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3
figures in main tex
Deep Learning approach predicting breast tumor response to neoadjuvant treatment using DCE-MRI volumes acquired before and after chemotherapy
Purpose: In breast cancer medical follow-up, due to the lack of specialized aided diagnosis tools, many breast cancer patients may continue to receive chemotherapy even if they do not respond to the treatment. In this work, we propose a new approach for early prediction of breast cancer response to chemotherapy from two follow-up DCE-MRI exams. We present a method that takes advantage of a deep convolutional neural network (CNN) model to classify patients who are responsive or non-responsive to chemotherapy. Methods and material: To provide an early prediction of breast cancer response to chemotherapy, we used a two branch Convolution Neural Network (CNN) architecture, taking as inputs two breast tumor MRI slices acquired before and after the first round of chemotherapy. We trained our model on a 693 x 2 ROIs belonging to 42 patients with local breast cancer. Image pretreatment, volumetric image registration and tumor segmentation were applied to MRI exams as a preprocessing step. As a ground truth, we used the anapathological standard reference provided of each patient. Results: Within 80 training epochs, an accuracy of 92.72% was obtained using 20% as validation data. The Area Under the Curve (AUC) was 0.96. Conclusion: In this paper, it was demonstrated that deep CNNs models can be used to solve breast cancer follow-up related problems. Therefore, the model obtained in this work can be exploited in future clinical applications after improving its efficiency with the used data.SCOPUS: cp.pDecretOANoAutActifinfo:eu-repo/semantics/publishe