Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

By Evangelia I. Zacharaki, Sumei Wang, Sanjeev Chawla, Dong Soo Yoo, Ronald L. Wolf, Elias R. Melhem, Christos Davatzikos. Magnetic Resonance in Medicine, 2009

826 citations recorded by the source corpus.

Subfields: Molecular Biology, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9a0ef2e00a28623c49b695ec6088ea1d965db257

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Robert James Gillies
2. Robert A. Gatenby
3. Nikos Paragios
4. Olivier Gevaert
5. M. López-Lázaro
6. J. Pouysségur

In Neurology

1. Sérgio Pereira
2. Carlos Alberto Silva
3. Victor Alves
4. Vishwesh Nath
5. Dong Yang
6. Holger R. Roth
7. Ali Hatamizadeh
8. Daguang Xu

In Radiology, Nuclear Medicine and Imaging

1. Dong Yang
2. Holger R. Roth
3. Victor Alves
4. Nikos Paragios
5. Robert A. Gatenby
6. Daguang Xu
7. Robert James Gillies
8. Olivier Gevaert

Citations in the corpus

• On Ensuring that Intelligent Machines Are Well-Behaved (2017, neutral): “…algorithms is important because, for example, they have been used for medical applications where undesirable behavior could delay cancer diagnoses [Zacharaki et al., 2009; Mangasarian et al., 1995], and because they have been shown to sometimes cause racist, sexist, and other discriminatory…”
• Support vector machines in DSC‐based glioma imaging: Suggestions for optimal characterization (2010, methodology): “In another study, a similar approach using texture features and SVMs for glioma grading also used a RBF kernel (17).”
• A survey of MRI-based brain tumor segmentation methods (2014, background): “For example, a group called Section of Biomedical Image Analysis (SBIA) has worked on these modalities for over 15 years [100,143] .”
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (2022, background): “High grade gliomas are an aggressive type of malignant brain tumors that grow rapidly and typically require surgery and radiotherapy and have poor survival prognosis [41].”
• Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, neutral): “Skull stripping is also performed on brain MRI images using a brain extraction tool (BET) and the extraction of brain tissues from other parts of skull [27].”
• Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? (2012, methodology): “The window Fourier transform of a 1D signal f (x) is given by: Fwðu; xÞ ¼ Z1 1 f ðxÞwðx xÞe j2puxdx ðE12Þ”
• Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “Anexampleoftheclas-sification of brain tumor histologic groups 25 demonstrated the usefulness of radiomic features to rapidly classify gliomas, metastases, and high-(grade III and grade IV) and low-grade (grade II) neoplasms.”
• Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018, methodology): “[17] predict the grade of gliomas from MRI images using a Support Vector Machine classifier.”
• A survey of MRI-based medical image analysis for brain tumor studies (2013, background): “Zou et al. (2004) compared the three different validation metrics: area under the receiver operating characteristics (ROC) curve, mutual information (MI) and Dice similarity coefficient (DSC) for probabilistic brain tumor segmentation.”