Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics

Chen, Qijian; Wang, Lihui; Deng, Zeyu; Zhang, Jian; Zhu, Yuemin

doi:10.1109/access.2020.3000895

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…There are also some studies that classify tumors based on multiple sequences (T1 with T1-Gd, T1 with T2, and others) in MRI ( Wu et al, 2019 ; Alis et al, 2020 ; Chen et al, 2020 ; Hamghalam et al, 2020 ; Lu et al, 2020 ; Zhang et al, 2020 ). However, most of these studies simply divide gliomas into high-grade gliomas (HGG) and low-grade gliomas (LGG).…”

Section: Related Workmentioning

confidence: 99%

Combining Radiology and Pathology for Automatic Glioma Classification

Wang

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen’s Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.

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Section: Related Workmentioning

confidence: 99%

Combining Radiology and Pathology for Automatic Glioma Classification

Wang

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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“…In another study, a wavelet scattering-based noise robust radiomic method was implemented to predict the gliomas grade in brain tumors. 74 …”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Beig

Bera

Tiwari

2020

Neuro-Oncology Advances

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Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

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Brain Tumor Classification with Multimodal MR and Pathology Images

Jia

2020

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics

Cited by 9 publications

References 41 publications

Combining Radiology and Pathology for Automatic Glioma Classification

Combining Radiology and Pathology for Automatic Glioma Classification

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Brain Tumor Classification with Multimodal MR and Pathology Images

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