Imaging of Neuronal and Mixed Glioneuronal Tumors

Razek, Ahmed Abdel Khalek Abdel; Elsebaie, Nermeen; Zamora, Carlos; Castillo, Maurício

doi:10.1097/rct.0000000000001010

Cited by 17 publications

(17 citation statements)

References 64 publications

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“…The most common pediatric posterior fossa subtypes include medulloblastoma, pilocytic astrocytoma, and ependymoma. Differentiation between the subtypes is crucial as each tumor has different management and prognosis [ 109 ]. Accurate preoperative diagnosis is required to tailor surgery and drug therapy.…”

Section: Radiomics Of Pediatric Brain Tumorsmentioning

confidence: 99%

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Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

et al. 2021

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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.

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Section: Radiomics Of Pediatric Brain Tumorsmentioning

confidence: 99%

“…Yet, definite tumor histopathological classification is done by biopsy. At present, radiomics and DL methods are developing non-invasive tumor classification models for predicting pediatric posterior fossa tumors [ 109 – 111 ].…”

Section: Radiomics Of Pediatric Brain Tumorsmentioning

confidence: 99%

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

et al. 2021

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“…GGs usually presents as a cyst with an enhancing mural nodule, with minimal to no surrounding edema and no significant mass effect. Foci of calcification are frequent (40-50%) in GGs and areas of cortical dysplasia can be seen adjacent to the tumor (28,29). On MRI, DNTs appear well-demarcated and frequently wedge shaped, hypointense on T1WI, and hyperintense on T2WI, lack of edema and mass effect.…”

Section: Patients and Definitionsmentioning

confidence: 99%

“…On MRI, DNTs appear well-demarcated and frequently wedge shaped, hypointense on T1WI, and hyperintense on T2WI, lack of edema and mass effect. Calcifications can be seen in 20% DNTs, and 20% DNTs have nodular or ring-like enhancement (28,29). Typical characteristics of GGs and DNTs on MRI imaging were considered as typical characteristics of GNTs (28,29).…”

Section: Patients and Definitionsmentioning

confidence: 99%

“…Calcifications can be seen in 20% DNTs, and 20% DNTs have nodular or ring-like enhancement (28,29). Typical characteristics of GGs and DNTs on MRI imaging were considered as typical characteristics of GNTs (28,29). Typical MRI characteristics of FCD or GNTs were considered when consensus between two independent neuroradiologists was achieved.…”

Section: Patients and Definitionsmentioning

confidence: 99%

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Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

et al. 2020

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Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

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2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist

et al. 2022

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The fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System (WHO CNS5) published in 2021 builds on the 2016 edition and incorporates output from the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy (cIMPACT-NOW). WHO CNS5 introduces fundamental changes to brain tumour classification through the introduction of new tumour families and types, especially in the paediatric population, and a revision of diagnostic criteria for some of the existing neoplasms.Neuroradiologists are central to brain tumour diagnostics, and it is therefore essential that they become familiar with the key updates. This review aims to summarise the most relevant updates for the neuroradiologist and, where available, discuss the known radiophenotypes of various new tumour types to allow for increased accuracy of language and diagnosis. Of particular importance, WHO CNS5 places greater emphasis on organising tumours by molecular type to reflect biology, as well as to allow for better planning of treatment. The principal updates in adult tumours concern

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Imaging of Neuronal and Mixed Glioneuronal Tumors

Cited by 17 publications

References 64 publications

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist

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