Fractal analysis of 11C-methionine PET in patients with newly diagnosed glioma

Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.

Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.

Glioblastoma and Methionine Addiction

Glioblastoma is a fatal brain tumor with a bleak prognosis. The use of chemotherapy, primarily the alkylating agent temozolomide, coupled with radiation and surgical resection, has provided some benefit. Despite this multipronged approach, average patient survival rarely extends beyond 18 months. Challenges to glioblastoma treatment include the identification of functional pharmacologic targets as well as identifying drugs that can cross the blood-brain barrier. To address these challenges, current research efforts are examining metabolic differences between normal and tumor cells that could be targeted. Among the metabolic differences examined to date, the apparent addiction to exogenous methionine by glioblastoma tumors is a critical factor that is not well understood and may serve as an effective therapeutic target. Others have proposed this property could be exploited by methionine dietary restriction or other approaches to reduce methionine availability. However, methionine links the tumor microenvironment with cell metabolism, epigenetic regulation, and even mitosis. Therefore methionine depletion could result in complex and potentially undesirable responses, such as aneuploidy and the aberrant expression of genes that drive tumor progression. If methionine manipulation is to be a therapeutic strategy for glioblastoma patients, it is essential that we enhance our understanding of the role of methionine in the tumor microenvironment.