Open AccessAdded value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis
Author(s) -
David Dreizin,
Tina Chen,
Yuanyuan Liang,
Yuyin Zhou,
Fabio M. Paes,
Yan Wang,
Alan Yuille,
Patrick Roth,
Kathryn Champ,
Guang Li,
Ashley McLenithan,
Jonathan J. Morrison
Publication year - 2021
Publication title -
abdominal radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.824
H-Index - 74
eISSN - 2366-004X
pISSN - 2366-0058
DOI - 10.1007/s00261-020-02892-x
Subject(s) - medicine , radiology , digital subtraction angiography , angiography , blunt , liver injury , trauma center , blunt trauma , retrospective cohort study , surgery
In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy for major arterial injury on digital subtraction angiography (DSA). American Association for the Surgery of Trauma (AAST) liver injury grading is coarse and subjective, with limited diagnostic utility in this setting. Volumetric measurements of hepatic injury burden could improve prediction. We hypothesized that in a cohort of patients that underwent catheter-directed hepatic angiography following admission trauma CT, a deep learning quantitative visualization method that calculates % liver parenchymal disruption (the LPD index, or LPDI) would add value to CE assessment for prediction of major hepatic arterial injury (MHAI).