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Original research
Performance of Radiomics derived morphological features for prediction of aneurysm rupture status
  1. Calvin Gerald Ludwig,
  2. Alexandra Lauric,
  3. Justin A Malek,
  4. Ryan Mulligan,
  5. Adel M Malek
  1. Department of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USA
  1. Correspondence to Dr Adel M Malek, Department of Neurosurgery, Tufts Medical Center, Boston, MA 02111, USA; amalek{at}tuftsmedicalcenter.org

Abstract

Background Morphological differences between ruptured and unruptured cerebral aneurysms represent a focus of neuroimaging researchfor understanding the mechanisms of aneurysmal rupture. We evaluated the performance of Radiomics derived morphological features, recently proposed for rupture status classification, against automatically measured shape and size features previously established in the literature.

Methods 353 aneurysms (123 ruptured) from three-dimensional rotational catheter angiography (3DRA) datasets were analyzed. Based on a literature review, 13 Radiomics and 13 established morphological descriptors were automatically extracted per aneurysm, and evaluated for rupture status prediction using univariate and multivariate statistical analysis, yielding an area under the curve (AUC) metric of the receiver operating characteristic.

Results Validation of overlapping descriptors for size/volume using both methods were highly correlated (p<0.0001, R2=0.99). Univariate analysis selected AspectRatio (p<0.0001, AUC=0.75), Non-sphericity Index (p<0.0001, AUC=0.75), Height/Width (p<0.0001, AUC=0.73), and SizeRatio (p<0.0001, AUC=0.73) as best among established descriptors, and Elongation (p<0.0001, AUC=0.71) and Flatness (p<0.0001, AUC=0.72) among Radiomics features. Radiomics Elongation correlated best with established Height/Width (R2=0.52), whereas Radiomics Flatness correlated best with Ellipticity Index (R2=0.54). Radiomics Sphericity correlated best with Undulation Index (R2=0.65). Best Radiomics performers, Elongation and Flatness, were highly correlated descriptors (p<0.0001, R2=0.75). In multivariate analysis, established descriptors (Height/Width, SizeRatio, Ellipticity Index; AUC=0.79) outperformed Radiomics features (Elongation, Maximum3Ddiameter; AUC=0.75).

Conclusion Although recently introduced Radiomics analysis for aneurysm shape and size evaluation has the advantage of being an efficient operator independent methodology, it currently offers inferior rupture status discriminant performance compared with established descriptors. Future research is needed to extend the current Radiomics feature set to better capture aneurysm shape information.

  • aneurysm
  • stroke
  • angiography
  • subarachnoid

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Footnotes

  • Twitter @calvin__ludwig

  • Contributors All authors are justifiably credited with authorship according to the authorship criteria. CGL: acquisition of the data, image processing, data analysis, and editing of the manuscript. AL: acquisition of the data, image processing, analysis and interpretation of the data, and drafting of the manuscript. JAM: acquisition of the data. RM: acquisition of the data. AMM: conception, design, acquisition of the data, analysis and interpretation of the data, critical revision, and final approval.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial, or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval This research was approved by Tufts Health Sciences Campus institutional review board (IRB No 9035). Patient consent was waived because of the retrospective nature of the study not impacting patient clinical care.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. Data are available upon reasonable request. Study data are from Tufts Medical Center clinical imaging repository, and as such contain patient identification, and cannot be shared in their raw state. De-identified models may be available upon reasonable request.

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