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Machine learning algorithms along with magnetic resonance imaging (MRI) provides promising techniques to overcome the drawbacks of the current clinical screening techniques. In this study the resting-state functional magnetic resonance imaging (fMRI) to see the level of activity in a patient's brain and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to explore the level of improvement of neo-adjuvant chemotherapy in patients with locally advanced breast cancer were considered. As the first project, we considered fMRI of patients before and after they underwent a double-blind smoking cessation treatment. For the first time, this study aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction in nicotine-dependent patients and future treatment efficacy. In this regards, two classes of patients have been studied, one took the drug N-acetylcysteine and the other took a placebo. Our goal was to classify the patients as treatment or non-treatment, based on their fMRI scans. The image slices of brain are used as the variable. We have applied different voxel selection schemes and data reduction algorithms on all images. Then, we compared several multivariate classifiers and deep learning algorithms and also investigated how the different data reductions affect classification performance. For the second part, we have employed multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this study. we determined the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death employing different machine learning algorithms.