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

Maeda, Yukito; Yamamoto, Yuka; Norikane, Takashi; Mitamura, Katsuya; Hatakeyama, Tetsuhiro; Miyake, Keisuke; Nishiyama, Yoshihiro; Kudomi, Nobuyuki

doi:10.1186/s40658-021-00418-y

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Furthermore, they found that the FD measure was able to classify LGG and HGG patients with 81% sensitivity and 89% specificity and concluded that FD can be a novel imaging biomarker for glioma. Functional 11C-methionine PET imaging was performed in Maeda et al (2021 , 11) that employed a fractal dimension approach in newly diagnosed glioma patients. They also observed a significant difference ( p < 0.001) in FD values between LGG and HGG patients.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Fractal dimension (FD) seems to be a measure of the fractal properties that describe the space-filling properties of networks, including biological systems. FD has been applied to histopathological studies to determine the complexity of certain tissue components 21 – 24 . Biscetti et al 25 measured FD and other parameters of superficial capillary plexus (SCP), intermediate capillary plexus (ICP), deep capillary plexus (DCP), and choriocapillaris of subjects with mild cognitive impairment (MCI) due to AD and cognitively healthy controls (CN).…”

Section: Related Workmentioning

confidence: 99%

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

Amin,

Elgammal,

Zahran

et al. 2023

Sci Rep

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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.

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“…The greater the overall accumulation of 11 C-MET, the shorter the patient survival. Maeda et al showed that fractal analysis of 11 C-MET PET in patients with newly diagnosed GBM could differentiate low-grade glioma and glioblastoma as well as indicated the IDH1 mutation status [39]. The fractal dimension measures the pattern of the radioactivity distribution in the region of interest.…”

Section: The Accumulation Of 11 C-methionine By Glioblastoma Tumor Ce...mentioning

confidence: 99%

Glioblastoma and Methionine Addiction

Sowers

2022

IJMS

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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.

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

Cited by 3 publications

References 14 publications

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

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

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

Glioblastoma and Methionine Addiction

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