Publications by authors named "M Adoui"
Article Synopsis
- The text discusses the challenges of using standardized treatments for patients with the same disease, highlighting the need for personalized medicine to address individual variability in reactions to treatments.
- It emphasizes the potential of AI and diagnostic tests to optimize treatment plans, improve prognosis accuracy, and automate monitoring to reduce errors commonly seen with traditional methods.
- The analysis of existing studies on breast cancer treatment response indicates that various AI technologies, including machine learning and imaging techniques, show promise in predicting patient outcomes and suggests future research directions in AI-driven oncology.
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.
View Article and Find Full Text PDFMulti-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
Int J Comput Assist Radiol Surg
September 2020
Purpose: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder's patients.
View Article and Find Full Text PDFBackground: Early prediction of nonresponse is essential in order to avoid inefficient treatments.
Purpose: To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.
Study Type: This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.
A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images.
Int J Comput Assist Radiol Surg
August 2018
Purpose: This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method.
Methods: PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy.