New CT-based dural ectasia criteria using machine learning to diagnose Marfan and Loeys-Dietz syndromes.

Aims: Dural ectasia (DE) is a criterion for Marfan syndrome (MFS) diagnosis. However, there is no agreement on its diagnosis. We identified new DE criteria on CT-scan imaging using machine-learning (ML) and aimed to evaluate their performance for MFS and Loeys-Dietz detection. Methods and results: MFS patients with FBN1 pathogenic variant, who underwent a complete CT-scan were included and matched 1:1 with controls. The conventional criteria for DE were assessed, as well as new criteria identified by ML (generalized linear model and random forest), regarding MFS diagnostic performance. The new DE criteria were then evaluated for identification of Loeys-Dietz patients with TGFßR1/2 and SMAD3 pathogenic variants. A user-friendly online interface was finally developed to predict the probability of MFS or Loeys-Dietz diagnosis.Between November 2010 and January 2017, 93 MFS patients and their matched controls were included. Mean age was 39 ± 13 years with 45% of women. Conventional definitions of DE showed poor diagnostic performance for MFS: AUC 0.68 (0.61-0.74).The new DE criteria, based on simple CT-scan measurements (antero-posterior diameter of the spinal canal and scalloping) on vertebrae L1, L2 and S1, achieved AUC 0.84 (0.69-0.94). These criteria outperformed the Ghent-criteria for MFS diagnosis. Moreover, in 46 patients with TGFßR1/2 and 40 with SMAD3 pathogenic variants, the new criteria also achieved good diagnostic performance (AUC 0.83 (0.73-0.90) and 0.80 (0.70-0.88) respectively). Conclusion: New CT-scan DE criteria showed good performance for detecting MFS or Loeys-Dietz syndromes. Given the importance of early diagnosis, these new simple criteria offer a promising screening tool.