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Project ID: P202208030002
Breast cancer diagnosis using an integrated radiomic framework and machine learning methods based on DCE-MRI images
Supervisor: Masoud Rezaei
Fund: Free
Status: on_going 7

Radiomics Feature, Machine Learning


• Machine Learning/ Deep learning (MATLAB\Python) • Medical Image expert • Radiomics Feature extraction

This project includes:


Worldwide, breast cancer is the most common cancer in women, including almost one-third of all females' malignancies. Previous studies indicate that various genetic and environmental factors could be correlated with breast cancer initiation and progression. Patients detected at an early stage will have a better survival rate. Therefore, a breast cancer diagnosis in the early stages is critical for its treatment. Breast MRI is a common image modality to assess the extent of disease in breast cancer patients. Recent studies show that MRI has the potential for prognosis of patients’ short and long-term outcomes as well as predicting pathological and genomic features of the tumors. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in breast cancer diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. In this study, we address this issue by presenting a comprehensive study of proliferation of a set of 922 patients and a radiomics method to classify malignant and benign breast tumors

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