Shear wave elastography‐based deep learning model for prognosis of patients with acutely decompensated cirrhosis

Abstract

AbstractPurposeThis study aimed to develop and validate a deep learning model based on two‐dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis.MethodsWe prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1‐year follow‐up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet‐50 to analyze 2D SWE images, a SWE‐based deep learning signature (DLswe) was developed for 1‐year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts.ResultsThe C‐index for DLswe was 0.748 (95% CI 0.666–0.829) and 0.744 (95% CI 0.623–0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C‐index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763–0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707–0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable.ConclusionsThe 2D SWE‐based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.