LAUSR

CT volumetry of blunt splenic injury-related features predicts splenectomy and angioembolization in adults and identifies clinically important target features for computer vision and automation research. Computed tomography volumetry of blunt splenic injury-related features predicts splenectomy and angioembolization in adults and identifies clinically important target features for computer vision and automation research. BACKGROUND Several ordinal grading systems are used in deciding whether to perform angioembolization (AE) or splenectomy following blunt splenic injury (BSI). The 2018 American Association for the Surgery of Trauma (AAST) Organ Injury Scale incorporates vascular lesions but not hemoperitoneum, which is considered in the Thompson classifier. Granular and verifiable quantitative measurements of these features may have a future role in facilitating objective decision making. PURPOSE The purpose of this study is to compare performance of computed tomography (CT) volumetry-based quantitative modeling to the 1994 and 2018 AAST Organ Injury Scale and Thompson classifier for the following endpoints: decision to perform splenectomy (SPY), and the composite of SPY or AE. MATERIALS AND METHODS Adult BSI patients (age ≥18 years) scanned with dual-phase CT prior to intervention at a single Level I trauma center from 2017 to 2019 were included in this retrospective study (n = 174). Scoring using 2018 AAST, 1994 AAST, and Thompson systems was performed retrospectively by two radiologists and arbitrated by a third. Endpoints included (1) SPY and (2) the composite of SPY or AE. Logistic regression models were developed from segmented active bleed, contained vascular lesion, splenic parenchymal disruption, and hemoperitoneum volumes. Area under the receiver operating characteristic curve (AUC) for ordinal systems and volumetric models were compared. RESULTS Forty-seven BSI patients (27%) underwent SPY, and 87 patients (50%) underwent SPY or AE. Quantitative model AUCs (0.85—SPY, 0.82—composite) were not significantly different from 2018 AAST AUCs (0.81, 0.88, p = 0.66, 0.14) for both endpoints and were significantly improved over Thompson scoring (0.76, p = 0.02; 0.77, p = 0.04). CONCLUSION Quantitative CT volumetry can be used to model intervention for BSI with accuracy comparable to 2018 AAST scoring and significantly higher than Thompson scoring. LEVEL OF EVIDENCE Prognostic and Epidemiological; Level IV.