ArticleJournal of medical imaging (Bellingham, Wash.)2019
Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.Cancers · 2023Review
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
- Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.IEEE transactions on medical imaging · 2022Article
- Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Radiomics in radiation oncology-basics, methods, and limitations.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and low-grade gliomas using multimodal MRI volumes (T1c, T2w, and FLAIR). RadCNN incorporates radiomic texture features (i.e., Haralick, Gabor, and Laws) within DeepMedic [a deep 3-D convolutional neural network (CNN) segmentation framework that uses image intensities; a top performing method in the BraTS 2016 challenge] to further augment the performance of brain tumor subcompartment segmentation. We first identify textural radiomic representations that best separate the different subcompartments [enhancing tumor (ET), whole tumor (WT), and tumor core (TC)] on the training set, and then feed these representations as inputs to the CNN classifier for prediction of different subcompartments. We hypothesize that textural radiomic representations of lesion subcompartments will enhance the separation of subcompartment boundaries, and hence providing these features as inputs to the deep CNN, over and above raw intensity values alone, will improve the subcompartment segmentation. Using a training set of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.