Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis

Xu, Jinhui; Lai, Mingyao; Li, Shaoqun; Ye, Kunlin; Li, Linzhen; Hu, Qingjun; Ai, Ruyu; Zhou, Jiangfen; Li, Juan; Zhen, Junjie; Cai, Linbo; Shi, Chuang

doi:10.1016/j.clineuro.2022.107478

Cited by 9 publications

(10 citation statements)

References 40 publications

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“…This may enable more confidence for empiric treatment with targeted therapies if tissue diagnosis is infeasible. pLGG mutational classification has been previously attempted in a few studies, most with manual segmentation-derived and/or pre-engineered radiomics (35)(36)(37)(38), which are known to fail when applied to the external dataset. Radiomic features have been extracted from MRI images and fitted to classifiers models like XGboost and SVM (17,35,36).…”

Section: Discussionmentioning

confidence: 99%

“…pLGG mutational classification has been previously attempted in a few studies, most with manual segmentation-derived and/or pre-engineered radiomics (35)(36)(37)(38), which are known to fail when applied to the external dataset. Radiomic features have been extracted from MRI images and fitted to classifiers models like XGboost and SVM (17,35,36). One study published in preprint, used neural networks to classify BRAF-mutational status in a single institution, though the algorithm required manual segmentation (16).…”

Section: Discussionmentioning

confidence: 99%

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Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Tak

Zapaishchykova

et al. 2023

Preprint

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PurposeTo develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG.Materials and MethodsWe conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: Boston Children’s Hospital (development dataset, N=214), and Child Brain Tumor Network (CBTN) (external validation, N=112). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wild-type) from whole-scan input via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist that quantifies the accuracy of model attention with respect to the tumor.ResultsA combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest AUC, taken as a weighted average across the three mutational classes, (0.82 [95% CI: 0.70-0.90], Accuracy: 77%) on internal validation and (0.73 [95% CI 0.68-0.88], Accuracy: 75%) on external validation. Training with TransferX also led to an AUC improvement of 17.7% and a COMDist Improvement of 6.42% over training from scratch on the development dataset.ConclusionTransfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.Summary StatementThe authors developed and externally validated an automated, scan-to-prediction deep learning pipeline that classifies BRAF Mutational status in pediatric low-grade gliomas directly from T2-Weighted MRI scans.Key ResultsAn innovative training approach combining self-supervision and transfer learning (“TransferX”) is developed to boost model performance in low data settings;TransferX enables the development of a scan-to-prediction pipeline for pediatric LGG mutational status (BRAF V600E, fusion, or wildtype) with high accuracy and mild performance degradation on external validation;An evaluation metric “COMDist” is proposed to increase interpretability and quantify the accuracy of the model’s attention around the tumor.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Tak

Zapaishchykova

et al. 2023

Preprint

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“…Most studies analyzed adult populations and high-grade gliomas, with only five studies (8%) analyzing pediatric populations [3][4][5][6][7], ten studies (16%) analyzing low-grade glioma [3,5,6,[8][9][10][11][12][13][14] and only four studies (6%) focusing on diffuse midline glioma [15][16][17][18]. Data on the analyzed patient population are shown in Table 2.…”

Section: Patient Populationmentioning

confidence: 99%

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Richter,

Ernemann,

Bender

2024

Cancers

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The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

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“…Magnetic resonance imaging (MRI) is the most important diagnostic imaging tool to study the morphological features of the tumor. 53 Ramaglia et al showed that mean apparent diffusion coefficient (ADC) and minimum ADC values measured by diffusion-weighted imaging could identify BRAF V600E mutations in PAs and gangliogliomas. 57 Lambin et al proposed the use of radiomics for a better understanding of the characteristics of the tumors.…”

Section: Braf Alterationsmentioning

confidence: 99%

“…53 There is a need for a noninvasive tool to obtain molecular profiling information for patients with pediatric LGGs that cannot be resected or biopsied. 53 Previous studies reported on the successful detection of BRAF V600E mutation using liquid biopsies such as serum, plasma, and cerebrospinal fluid. [54][55][56] However, these studies were only case reports or small case series.…”

Section: Prediction and Detection Of Braf V600e Mutationmentioning

confidence: 99%

BRAF Landscape and Its Implications among Patients with Pediatric Low-Grade Gliomas: A Comprehensive Review of the Literature

Fouda

2023

Journal of Pediatric Neurology

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Low-grade gliomas are the most common intracranial tumor in the pediatric population. Pediatric low-grade gliomas represent a heterogeneous group of tumors. Genetic alterations that result in upregulation of the MAPK/ERK pathway represent most of the genetic landscape of pediatric low-grade gliomas. BRAF-V600E mutant pediatric low-grade gliomas may represent a unique and aggressive subset of tumors that require targeted therapy especially if gross total resection is not feasible. Many patients with pediatric low-grade gliomas have demonstrated successful clinical and radiological responses to BRAF and/or MEK inhibitors. Given the high proportion of patients who fail to respond to the current standard chemotherapy and radiotherapy, these targeted therapies should be considered in future trials and further investigations. In this review of the literature, we summarize the molecular status of BRAF alterations among patients with pediatric low-grade gliomas and provide an update on previous and current BRAF and MEK inhibitors clinical trials.

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Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis

Cited by 9 publications

References 40 publications

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

BRAF Landscape and Its Implications among Patients with Pediatric Low-Grade Gliomas: A Comprehensive Review of the Literature

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