Nomogram based on CT-derived extracellular volume for the prediction of post-hepatectomy liver failure in patients with resectable hepatocellular carcinoma.

OBJECTIVES

This study aimed to develop and validate a nomogram based on extracellular volume (ECV) derived from computed tomography (CT) for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC).

METHODS

A total of 202 patients with resectable HCC from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. One hundred twenty-one patients from our hospital and 81 patients from another hospital were assigned to the training cohort and the validation cohort, respectively. CT-derived ECV was measured using nonenhanced and equilibrium-phase-enhanced CT images. The nomogram was developed with independent predictors of PHLF. Predictive performance and calibration were assessed by receiver operator characteristic (ROC) analysis and Hosmer-Lemeshow test, respectively. The Delong test was used to compare the areas under the curve (AUCs).

RESULTS

CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver (p < 0.001, r = 0.591). The nomogram combining CT-derived ECV, serum albumin (Alb), and serum total bilirubin (Tbil) obtained higher AUCs than the albumin-bilirubin (ALBI) score for predicting PHLF in both the training cohort (0.828 vs. 0.708; p = 0.004) and the validation cohort (0.821 vs. 0.630; p < 0.001). The nomogram showed satisfactory goodness of fit for PHLF prediction in the training and validation cohorts (p = 0.621 and 0.697, respectively).

CONCLUSIONS

The nomogram contributes to the preoperative prediction of PHLF in patients with resectable HCC.

KEY POINTS

• CT-derived ECV had a strong correlation with the postoperative pathological fibrosis stage of the background liver. • CT-derived ECV was an independent predictor of PHLF in patients with resectable HCC. • The nomogram based on CT-derived ECV showed a superior prediction efficacy than that of clinical models (including Child-Pugh stage, MELD score, and ALBI score).