Differentiating between giant cell arteritis and atherosclerosis on [18F]FDG-PET: An explainable machine learning approach

H.S. Vries, G.D. Van Praagh, P.H. Nienhuis, O. Bouhali, R. H.J.A. Slart, L. Alic

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Background This work aims to investigate the feasibility of an explainable machine learning model based on radiomics features to differentiate between giant cell arteritis (GCA) and atherosclerosis in aortic [18F]FDG-PET scans. Method Twenty [18F]FDG-PET scans (ten of patients with GCA, ten with atherosclerosis) were retrospectively included. The aorta was delineated into four segments (ascending, arch, descending, and abdominal aorta). In total, 93 radiomic features and two quantitative features were extracted from each of the 80 segments. Four different feature selection methods and four classifiers were used to identify important features for the machine learning model and determine the probability. The model's performance was evaluated using accuracy and AUC. To enhance explainability of the model, feature importance was determined, and an occlusion sensitivity map of the aorta was created. Results The combination of the first-order skewness, GLDM dependence non-uniformity, and GLRLM run entropy features showed the highest accuracy and AUC of, 0.90±0.08 and 0.960±0.029, respectively. Conclusion This study demonstrated the potential of an explainable radiomics-based machine learning model for the differentiation between GCA and atherosclerosis in P8F]FDG-PET scans.

Original languageEnglish
Title of host publication2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)
EditorsRosa Sicilia, Bridget Kane, Joao Rafael Almeida, Myra Spiliopoulou, Jose Alberto Benitez Andrades, Giuseppe Placidi, Alejandro Rodriguez Gonzalez
PublisherIEEE
Pages870-875
Number of pages6
ISBN (Electronic)979-8-3503-1224-9
ISBN (Print)979-8-3503-1225-6
DOIs
Publication statusPublished - 17 Jul 2023
Event36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023 - L'Aquila, Italy
Duration: 22 Jun 202324 Jun 2023
Conference number: 36
https://2023.cbms-conference.org/

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2023-June
ISSN (Print)1063-7125

Conference

Conference36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023
Abbreviated titleCBMS 2023
Country/TerritoryItaly
CityL'Aquila
Period22/06/2324/06/23
Internet address

Keywords

  • atherosclerosis
  • explainable machine learning
  • giant cell arteritis
  • radiomics
  • [18F]FDG-PET
  • 2023 OA procedure

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