Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of <i>BRAF-</i>Mutated and <i>BRAF</i>-Fused Tumors

Wagner, Matthias; Hainc, Nicolin; Khalvati, Farzad; Namdar, Khashayar; Figueiredo, Luciana F.; Sheng, Min; Laughlin, Suzanne; Shroff, Manohar; Bouffet, Éric; Tabori, Uri; Hawkins, Cynthia; Yeom, Kristen W.; Ertl‐Wagner, Birgit

doi:10.3174/ajnr.a6998

Cited by 50 publications

(38 citation statements)

References 44 publications

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“…RF has been a popular data mining and statistical tool in radiomics research due to its transparency and success in classi cation and regression tasks (20,(30)(31)(32)(33). It generates a large number of decision trees using random subsamples of the training data while also randomly varying the features used in the trees (34).…”

Section: Discussionmentioning

confidence: 99%

“…The datasets of 94 patients from The Hospital for Sick Children, Toronto, and 21 patients from The Lucile Packard Children's Hospital, Stanford, used in this study have been previously published (20). The previous study applied an RF model without variations in sample size to differentiate BRAF fused from BRAF V600E mutated pLGG.…”

Section: Methodsmentioning

confidence: 99%

“…Radiomic signatures have been extensively investigated for different cancer sites including liver cancer (15), bone tumors (16), glioblastoma (17), medulloblastoma (21), and midline high-grade glioma (18,19). Recently, we applied Random Forest (RF) to differentiate BRAF fused from BRAF V600E mutated pLGG and yielded an area under the receiver operating characteristic curve (AUC) of 0.85 on an independent validation set (20). It has not been well established to what extent different classi cation models and the size of the training data affect diagnostic performance.…”

Section: Introductionmentioning

confidence: 99%

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Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

Wagner

Namdar

Alqabbani

et al. 2021

Preprint

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Machine learning (ML) approaches can predict BRAF status of pediatric low-grade gliomas (pLGG) on pre-therapeutic brain MRI. The impact of training data sample size and type of ML model is not established. In this bi-institutional retrospective study, 251 pLGG FLAIR MRI datasets from 2 children’s hospitals were included. Radiomics features were extracted from tumor segmentations and five models (Random Forest, XGBoost, Neural Network (NN) 1 (100:20:2), NN2 (50:10:2), NN3 (50:20:10:2)) were tested to classify them. Classifiers were cross-validated on data from institution 1 and validated on data from institution 2. Starting with 10% of the training data, models were cross-validated using a 4-fold approach at every step with an additional 2.25% increase in sample size. Two-hundred-twenty patients (mean age 8.53 ± 4.94 years, 114 males, 67% BRAF fusion) were included in the training dataset, and 31 patients (mean age 7.97±6.20 years, 18 males, 77% BRAF fusion) in the independent test dataset. NN1 (100:20:2) yielded the highest area under the receiver operating characteristic curve (AUC). It predicted BRAF status with a mean AUC of 0.85, 95% CI [0.83, 0.87] using 60% of the training data and with mean AUC of 0.83, 95% CI [0.82, 0.84] on the independent validation data set.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

Wagner

Namdar

Alqabbani

et al. 2021

Preprint

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“…BRAF status was predicted with an AUC of 0.75 (SD, 0.12) and (95% confidence interval 0.62–0.89) for the internal validation cohort through a fourfold cross-validation scheme, and AUC for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status, while sex was not a significant predictor [ 119 ].…”

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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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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“…A recent study showed the ability of radiomics-based prediction of BRAF status in pediatric low-grade gliomas before a biopsy, which could have significant implications in clinical practice. 91 Immunology. Use of immunotherapeutic strategies, including adoptive cell therapy, vaccine therapy, and checkpoint inhibitors, in pediatric brain tumors has been rapidly evolving.…”

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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Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors

Cited by 50 publications

References 44 publications

Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

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

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

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