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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: May 31, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.

Melike Bilgin¹, Sabriye Sennur Bilgin¹, Burak Han Akkurt¹

¹Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany.

|January 25, 2025

Summary

This summary is machine-generated.

Machine learning models can accurately differentiate low-grade and high-grade gliomas using computed tomography (CT) scans, not just MRI. This advance may speed up brain tumor diagnosis and reduce biopsies.

Keywords:

CT artificial intelligence glioma grading machine learning neuroimaging radiomics

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Area of Science:

Neuro-oncology
Radiology
Artificial Intelligence

Background:

Machine learning (ML) is increasingly used for central nervous system (CNS) tumor grading.
Most studies utilize magnetic resonance imaging (MRI) or positron emission tomography (PET) data.
Few studies have explored ML for tumor grading using computed tomography (CT) scans.

Purpose of the Study:

To assess the feasibility of ML-based tumor diagnosis using CT images.
To investigate the differentiability of low-grade and high-grade gliomas with CT scans.
To compare the efficacy of CT versus MRI for ML-based glioma grading.

Main Methods:

Histologically confirmed gliomas were analyzed.
Three conventional ML algorithms and a neural network were evaluated.
Performance was compared against existing ML studies using MRI data.

Main Results:

The best model utilized six features with a Naive Bayes approach.
The model achieved a mean Area Under the Curve (AUC) of 0.903.
High diagnostic performance was observed: accuracy (0.839), sensitivity (0.807), and specificity (0.864).

Conclusions:

ML algorithms can effectively differentiate low-grade and high-grade gliomas using CT images.
CT-based ML models offer a promising alternative to MRI for brain tumor diagnostics.
Future CT-based models could accelerate diagnosis and decrease the need for biopsies.