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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.

Andreas Stadlbauer^1,2, Franz Marhold³, Stefan Oberndorfer⁴

¹Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria.

|May 28, 2022

Summary

Machine learning algorithms applied to advanced and physiological MRI data can reliably classify contrast-enhancing brain tumors. This radiophysiomics approach shows promise for improving clinical diagnosis, though deep neural networks may be needed for efficient data preprocessing.

Keywords:

artificial intelligence brain tumors machine learning multiclass classification neuro-oncology physiological MRI pretreatment classification

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Area of Science:

Neuroimaging
Artificial Intelligence
Oncology

Background:

Accurate characterization of contrast-enhancing brain tumors is crucial for patient outcomes.
Advanced MRI (advMRI) and physiological MRI (phyMRI) offer enhanced diagnostic capabilities but increase data complexity.
Artificial intelligence (AI) presents solutions for managing complex neuroimaging data analysis.

Purpose of the Study:

To investigate the efficacy of multiclass machine learning (ML) algorithms in classifying contrast-enhancing brain tumors using radiomic features from advMRI and phyMRI (radiophysiomics).

Main Methods:

A training cohort of 167 patients with five common brain tumor types was used to develop 135 ML classifiers.
Nine common ML algorithms were combined with advMRI and phyMRI data.
Performance was evaluated using tenfold cross-validation and an independent test cohort.

Main Results:

Adaptive boosting and random forest algorithms using advMRI and phyMRI data achieved superior accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), and AUROC (0.886 vs. 0.813) compared to human readers.
Radiologists demonstrated higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902).

Conclusions:

ML-based radiophysiomics shows potential for aiding in the clinical diagnosis of contrast-enhancing brain tumors.
Further integration of deep neural networks is recommended to address the time and work involved in data preprocessing.