This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient’s prognoses.
Background: Diffusion tensor imaging (DTI) is a non-invasive MR modality that provides an evaluation of brain tissue microstructure and architecture in vivo. We aimed to assess the diagnostic value of DTI parameters in evaluating cerebral white matter integrity in patients of severe chronic obstructive pulmonary disease (COPD) and correlate these parameters with smoking index (SI) and the number of exacerbations in the last year. This prospective study included 30 COPD male past smoker patients and 15 age-and sex-matched nonsmoker controls. Staging of COPD, SI and number of exacerbations in the last year were obtained. Routine brain MRI and DTI were done in all subjects. The selected white matter tracts' fractional anisotropy (FA), and mean diffusivity (MD) were calculated in the region of interest in axial slices. Results: The mean FA and MD values of all selected white matter tracts showed a high significant difference (p < 0.001) between patients and control group. The correlation between FA, SI and exacerbation frequency was not significant in the majority of white matter tracts (p > 0.05). The correlation between MD, SI and exacerbation frequency was significant for the majority of tracts (p < 0.05). Conclusion: DTI metrics are valuable non-invasive tools in evaluating the white matter abnormalities in COPD patients. Smoking index and frequency of exacerbations have possible relation to extra-pulmonary cerebral manifestations of COPD.
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