A novel machine learning-based radiomic model for diagnosing high bleeding risk esophageal varices in cirrhotic patients.

Abstract:

BACKGROUND AND AIM

To develop and validate a novel machine learning-based radiomic model (RM) for diagnosing high bleeding risk esophageal varices (HREV) in patients with cirrhosis.

METHODS

A total of 796 qualified participants were enrolled. In training cohort, 218 cirrhotic patients with mild esophageal varices (EV) and 240 with HREV RM were included to training and internal validation groups. Additionally, 159 and 340 cirrhotic patients with mild EV and HREV RM, respectively, were used for external validation. Interesting regions of liver, spleen, and esophagus were labeled on the portal venous-phase enhanced CT images. RM was assessed by area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, calibration and decision curve analysis (DCA).

RESULTS

The AUROCs for mild EV RM in training and internal validation were 0.943 and 0.732, sensitivity and specificity were 0.863, 0.773 and 0.763, 0.763, respectively. The AUROC, sensitivity, and specificity were 0.654, 0.773 and 0.632, respectively, in external validation. Interestingly, the AUROCs for HREV RM in training and internal validation were 0.983 and 0.834, sensitivity and specificity were 0.948, 0.916 and 0.977, 0.969, respectively. The related AUROC, sensitivity and specificity were 0.736, 0.690 and 0.762 in external validation. Calibration and DCA indicated RM had good performance. Compared with Baveno VI and its expanded criteria, HREV RM had a higher accuracy and net reclassification improvements that were as high as 49.0% and 32.8%.

CONCLUSION

The present study developed a novel non-invasive RM for diagnosing HREV in cirrhotic patients with high accuracy. However, this RM still needs to be validated by a large multi-center cohort.

© 2022. Asian Pacific Association for the Study of the Liver.

Keywords: Cirrhotic portal hypertension, Computed tomography, Endoscopy, Esophageal varices, Machine learning, Radiomic model

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Authors

• Zhenjia Ding |
• Huiguo Ding |
• Chunlei Fan |
• Keqin Hu |
• Tehui Huang |
• Yue Li |
• Lei Li |
• Zhi Wang |
• Yijie Yan |
• Yuening Zhang |
• Shibin Zhang |