Prognostic stratification for IDH-wild-type lower-grade astrocytoma by Sanger sequencing and copy-number alteration analysis with MLPA

Makino, Yasuhide; Arakawa, Yoshiki; Yoshioka, Ema; Shofuda, Tomoko; Kawauchi, Takeshi; Terada, Yukinori; Tanji, Masahiro; Kanematsu, Daisuke; Mineharu, Yohei; Miyamoto, Susumu; Kanemura, Yonehiro

doi:10.1038/s41598-021-93937-8

Cited by 15 publications

(8 citation statements)

References 39 publications

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“…Tumor pathology was confirmed at the time of surgical removal of the tumor. In the present study, 6 patients had IDH1 wild type gliomas, and 12 patients had IDH1-mutant gliomas 54 . Tumor masks of all subjects in Montreal Neurological Institute (MNI) space were then stacked and binarized to construct a tumor-overlapping image using the FMRIB Software Library ( www.fmrib.ox.ac.uk/fsl ) 55 (Fig.…”

Section: Methodsmentioning

confidence: 41%

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

Ueda

Usami

Yamao

et al. 2022

Sci Rep

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The association between neurocognitive function (NCF) impairment and brain cortical functional connectivity in glioma patients remains unclear. The correlations between brain oscillatory activity or functional connectivity and NCF measured by the Wechsler Adult Intelligence Scale full-scale intelligence quotient scores (WAIS FSIQ), the Wechsler Memory Scale-revised general memory scores (WMS-R GM), and the Western aphasia battery aphasia quotient scores (WAB AQ) were evaluated in 18 patients with left frontal glioma using resting-state electroencephalography (EEG). Current source density (CSD) and lagged phase synchronization (LPS) were analyzed using exact low-resolution electromagnetic tomography (eLORETA). Although 2 and 2 patients scored in the borderline range of WAIS FSIQ and WMS-R GM, respectively, the mean WAIS FSIQ, WMS-R GM, and WAB AQ values of all patients were within normal limits, and none had aphasia. In the correlation analysis, lower WMS-R GM was associated with a higher LPS value between the right anterior prefrontal cortex and the left superior parietal lobule in the beta1 band (13–20 Hz, R = − 0.802, P = 0.012). These findings suggest that LPS evaluated by scalp EEG is associated with memory function in patients with left frontal glioma and mild NCF disorders.

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Section: Methodsmentioning

confidence: 41%

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

Ueda

Usami

Yamao

et al. 2022

Sci Rep

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“…In the study of Makino et al (2022) the features of IDHwildtype lower-grade astrocytoma remain unknown. According to the third cIMPACTNOW update, IDH-wildtype astrocytomas with any of the following characteristics have a poor prognosis: chromosome 7 gain and 10 loss (+ 7/10), EGFR amplification, and/or TERT promoter (TERTp) mutation [10]. Multiplex ligation-dependent probe amplification (MLPA) is a low-cost method for detecting copy number changes.…”

Section: Idh1/2-mutant Diffuse Astrocytoma Vs Idh-wildtype Diffuse As...mentioning

confidence: 99%

Комплексний Огляд Дифузної Низькодиференційованої Астроцитоми: Характеристика, Молекулярна Класифікація І Хірургічне Лікування

Chaulagain¹,

Smolanka²,

Smolanka³

2022

INJ

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Дифузна астроцитома (ДА) — це рідкісна низькодиференційована астроцитома з високою клітинною диференціацією, повільним ростом і великою інфільтрацією сусідніх ділянок мозку. Незважаючи на те, що Всесвітня організація охорони здоров’я класифікує їх як дифузні астроцитоми ІІ ступеня, ці новоутворення в дітей клінічно і молекулярно відрізняються від таких у дорослих. Вони рідко переходять до ураження вищого ступеня і рідко мають мутацію IDH. ДА найчастіше зустрічаються в молодих людей, хоча також можуть виникнути в молодшій популяції і в людей похилого віку. Їх можна виявити всюди в мозку, але найчастіше вони зустрічаються в півкулях головного мозку — «мисленнєвому» відділі. Краї дифузної астроцитоми мають тенденцію розширюватися в оточуючі нормальні тканини головного мозку, на що вказує термін. Судоми і мігрені часто є першими симптомами цієї пухлини, як і параліч однієї сторони тіла (геміпарез). У цій статті ми обговорюємо клінічні, гістологічні й молекулярні характеристики дифузної астроцитоми ІІ ступеня, наголошуючи на її діагностичних критеріях, поширеності в ділянках мозку, найбільш поширених молекулярних характеристиках і способах скринінгу, а також вплив хірургічної резекції ДА на лікування.

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“…A considerable number of patients could be misjudged with contrast-enhanced MRI, especially the patients with IDH-wildtype anaplastic glioma [ 12 ]. IDH (isocitrate dehydrogenase) is an important enzyme in the tricarboxylic acid cycle, and the tumors with normal IDH genes are referred to as “IDH wild-type” or “IDH negative” [ 13 ]. These IDH wild-type tumors are considered the most aggressive ones and lack contrast enhancement on MRI, so it may not be the best option for resection guidance [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

et al. 2022

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Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

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Prognostic stratification for IDH-wild-type lower-grade astrocytoma by Sanger sequencing and copy-number alteration analysis with MLPA

Cited by 15 publications

References 39 publications

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

Комплексний Огляд Дифузної Низькодиференційованої Астроцитоми: Характеристика, Молекулярна Класифікація І Хірургічне Лікування

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

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