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Aneurysm management involves either conservative medical therapy or surgical intervention, depending on the size and symptoms of the aneurysm. Conservative management is generally reserved for smaller, asymptomatic aneurysms, while larger or symptomatic aneurysms often necessitate surgical repair.Conservative Medical TherapyFor small, asymptomatic aneurysms, particularly abdominal aortic aneurysms (AAA) less than 5.5 centimeters in diameter, conservative medical therapy is recommended. This...
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CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.

Osamah Alwalid1,2, Xi Long1,2, Mingfei Xie1,2

  • 1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Neurology
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Summary
This summary is machine-generated.

This study identified key radiomics features from CT angiography scans to accurately classify intracranial aneurysms as ruptured or unruptured. The developed radiomics model shows promise for improving the early detection of aneurysm rupture.

Keywords:
aneurysm ruptureintracranial aneurysmmachine learningradiomicssubarachnoid hemorrhage

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Intracranial aneurysm rupture leads to high mortality and morbidity.
  • Early detection and management of ruptured aneurysms are critical.
  • Radiomics analysis offers a novel approach to characterizing vascular lesions.

Purpose of the Study:

  • To identify radiomics features predictive of intracranial aneurysm rupture.
  • To develop and validate a radiomics-based classification model for aneurysm rupture.
  • To assess the performance of the radiomics model in distinguishing ruptured from unruptured aneurysms.

Main Methods:

  • Radiomics analysis of CT angiography (CTA) images from 393 patients.
  • Feature selection and logistic regression model construction.
  • Validation of the radiomics model on independent training and test cohorts.

Main Results:

  • Nine radiomics features were selected to build the classification model.
  • The radiomics model achieved high performance in classifying aneurysm rupture (AUC 0.92 training, 0.86 test).
  • Significant differences in radiomics scores were observed between ruptured and unruptured aneurysms.

Conclusions:

  • Aneurysm radiomics features show potential for automatic classification of rupture on CTA.
  • This approach may aid in the timely diagnosis and management of intracranial aneurysms.
  • Radiomics holds promise for improving diagnostic accuracy in neurovascular imaging.