Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach

Jie, Dong; Li, Lei; Liang, Shengxiang; Zhao, S. J.; Zhang, Bin; Meng, Yang; Zhang, Yong; Li, Suxiao

doi:10.1016/j.acra.2020.02.012

Cited by 35 publications

(46 citation statements)

References 38 publications

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“…Several studies have shown the feasibility of PFT classification using conventional machine learning and MRI data. A few studies used histogram textural analysis and visual based features extracted from diffusion and conventional MRI, and patients' clinical features [6,[16][17][18][19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

et al. 2021

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Posterior fossa tumors (PFT) are the most common tumors in children. Differentiation between the various PFT types is critical, as different tumors have diverse treatment approaches. This study proposes the use of fused architecture comprising two neural networks, a pre-trained ResNet-50 Convolutional Neural Network (CNN) and a tabular based network for the classification of PFT. The study included data for 158 MRI scans of 22 healthy controls and 136 pediatric patients with newly diagnosed PFT (63 Pilocytic Astrocytoma, 57 Medulloblastoma and 16 Ependymoma). The input data for classification were from magnetic resonance imaging: post contrast T1-weighted, fluid attenuated inversion recovery and diffusion Trace images, and tabular data: subject's age. Evaluation of the model was performed in a stratified 5-fold cross-validation manner, based on accuracy, precision, recall and F1 score metrics. Model explanation was performed in terms of visual explanation of the CNN by Gradient-weighted Class Activation Mapping (Grad-CAM) and by testing the contribution to the classification results of the different imaging input data sets and the proposed fused architectures relative to CNN only and tabular only architectures. The best classification results were obtained with the fused CNN + tabular data architecture, and based on diffusion Trace images, achieving mean cross-validation accuracy of 0.88±0.04 for the validation and 0.87±0.02 for the test dataset. Overall, the proposed architecture achieved improvement in accuracy and F1 score compared to CNN method for this dataset.

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Section: Related Workmentioning

confidence: 99%

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

et al. 2021

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“…A relatively low prevalence and the specifics of pediatric populations meant that only 11 studies were identified during the search. Radiomic-based discrimination between adult and pediatric tumor entities (ependymoma, pilocytic astrocytoma, and MBs) have been done with promising accuracy and AUCs of over 0.9 in certain studies [63][64][65][66][67][68]. T1CE sequences and ADC maps were used in most of the models.…”

Section: Medulloblastoma and Other Tumors Of The Posterior Fossamentioning

confidence: 99%

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Kalasauskas

Kosterhon

Keric

et al. 2022

Cancers

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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

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“…83 Unlike adults, in whom we have seen increased use of noninvasive neuroimaging using multimodal MR imaging combining multiple sequences to enhance tissue characterization, few studies to date have been reported in pediatrics. 5,9,[84][85][86][87] Similar noninvasive imaging studies are needed in parallel with the evolving molecular era in pediatric brain tumors, facilitating improved tumor classification (histologic and molecular), grading, survival prediction, and treatment response.…”

Section: Evolving Era Of Radiomics In Pediatric Neuro-oncologymentioning

confidence: 99%

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

Toll

Hein

et al. 2021

AJNR Am J Neuroradiol

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Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

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Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach

Cited by 35 publications

References 38 publications

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

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