Novel radiomics interpretation pipeline with feature extraction and radiomics

Abstract

The advancements in neuroimaging techniques, predominantly magnetic resonance imaging (MRI) and computed tomography (CT) scans, have undeniably reformed the clinical neuroscience landscape by escalating the accuracy and reliability of both diagnosis and prognosis in the realm of intricate neurological disorders. However, the quest to further augment these advancements continues, igniting a compelling interest in the conception and integration of sophisticated machine learning models which potentiate swift and precise three-dimensional reconstruction of neuronal particles, thereby aiding medical practitioners in formulating efficacious therapeutic interventions. To actualize this motive, the present thesis proposed an innovative methodology, underpinned by an interpretive simulation which in turn is a corollary of the accrued model assessment results. The methodological design of this investigative endeavor amalgamates robust imaging pipelines such as generative adversarial networks (GANs) with precision-calibrated deep learning networks, featuring a meticulously designed densenet-41-based backbone in tandem with CornerNet. For the pragmatic application of this scientific inquiry, a selected sample set derived from the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020 dataset, embodying brain MRI images, was exploited. Moreover, insights from the RSNA-MICCAI Brain Tumor Radiogenomic Classification challenge were assimilated, focusing on predicting MGMT promoter methylation status, an essential biomarker in brain tumor treatments. The experimental design delineated 100 images as the training set, complemented by an auxiliary set of 10 images demarcated for validation purposes. Findings from this research exude a striking classification efficacy of 91.2% for the densenet-41-based backbone and 89.8% for CornerNet, ascertaining the hitherto diagnostic precision of this methodology. In the interest of fortifying classification accuracy, a Time-Lapse analysis was incorporated for sequential scrutiny, alongside the induction of a Long Short-Term Memory (LSTM) layer responsible for processing voxels in a sequence, thereby augmenting the precision of tracking from one voxel to another. In conclusion, the outcomes derived from this pioneering study corroborate that the novel approach substantially optimizes both velocity and precision in the diagnosis and prognosis of neurological disorders. The empirical evidence garnered aligns impeccably with the relevant academic and clinical benchmarks, providing a robust platform for quantitative analysis and facilitating the classification of an array of brain tumor types, potentially leading to transformative outcomes in neuro-oncology treatment paradigms.