Diagnostic accuracy of 3D imaging combined with intra-operative ultrasound in the prediction of post-hepatectomy liver failure.

BACKGROUND

The risk of post-hepatectomy liver failure (PHLF) is difficult to predict preoperatively. Accurate preoperative assessment of residual liver volume is critical in PHLF. Three-dimensional (3D) imaging and intra-operative ultrasound (IOUS) offer significant advantages in calculating liver volume and have been widely used in hepatectomy risk assessment. Our research aimed to explore the accuracy of 3D imaging technique combining IOUS in predicting PHLF after hepatectomy.

METHODS

We used a retrospective study design to analyze patients who underwent hepatectomy with 3D imaging combined with IOUS between 2017 and 2020. Utilizing 3D reconstruction, the patient's residual liver volumes (PRLVs) and ratio of PRLV to standard liver volume (SLV) were calculated preoperatively. Hepatectomy were performed and actual hepatectomy volume (AHV) were measured. Consistency between preoperative planned hepatectomy volume (PPHV) and AHV was quantified postoperatively by Bland-Altman analysis. Multiple logistic regression and receiver-operating characteristic (ROC) curves were utilized to discuss the predictive value of PRLV/SLV in PHLF.

RESULTS

Among the 214 included patients, 58 (27.1%) had PHLF. Patients with PHLF had significantly higher residual rates of ICG-R15 (%) (P=0.000) and a lower PRLV/SLV ratio (P=0.000). Bland-Altman analysis showed that PPHV was consistent with AHV (P=0.301). Multivariate analysis confirmed that PRLV/SLV ratio >60% (OR, 0.178; 95% CI: 0.084-0.378; P<0.01) was a protective factor for PHLF. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.8% (95% CI: 64.5.3-87.2%), 66.6% (95% CI: 59.1-74.1%), 45.8%, and 88.1%, respectively. The area under the ROC curve (AUC) was 73.7% (95% CI: 65.7-85.8%) and the diagnostic accuracy of PRLV/SLV for PHLF was moderate (P<0.001). These results were validated in the validation cohort perfectly. The primary cohort included 214 patients with a PHLF rate of 27.1% (n=58, 28 grade B and 13 grade C). The validation cohort included 135 patients with a PHLF rate of 35.6% (n=48, 24 grade B and 11 grade C).

CONCLUSIONS

The calculation of PRLV/SLV has predictive value in PHLF and can be exploited as a predictive factor. The 3D imaging technique combined with IOUS may be useful for PHLF risk assessment in hepatectomy patients.