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Novel Angiographic Scores for evaluation of Large Vessel Vasculitis.

ABSTRACT: Arterial involvement is the cardinal feature of large-vessel vasculitis (LVV) and prevention of disease progression is the principal therapeutic goal. However, development of tools for its evaluation represents a major unmet need. To address this, a widely-applicable imaging tool for LVV, analysing arterial involvement in 17 arterial territories, has been developed and validated. Individual stenosis and dilation scores were generated and combined in a composite score. The methodology was validated cross-sectionally and longitudinally in 131 patients, 96 Takayasu arteritis (TA), 35 large-vessel giant-cell arteritis (LV-GCA). In total, 4420 arterial segments from 260 imaging studies were evaluated. The new scores allowed quantitative grading of LVV arterial involvement with high consistency, revealing inter-patient differences. TA had higher stenosis and composite scores and lower dilation scores than LV-GCA. Baseline stenotic and composite scores reflected arterial damage rather than disease-activity. Longitudinal changes in all three scores correlated with disease activity and mirrored arterial disease evolution, reflecting both progressive injury and lesion improvement. Increases ≥1 in any score were specific for arterial disease progression. The scores objectively quantify arterial involvement in LVV, providing precise definition of disease phenotype and evolution. We propose that they represent novel vascular outcome measures essential for future clinical trials.

SUBMITTER: Tombetti E

PROVIDER: S-EPMC6206009 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposeAs "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics.MethodsWe automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables).ResultsFor discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.ConclusionMachine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.

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Novel Angiographic Scores for evaluation of Large Vessel Vasculitis.

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