Predictive value of combined computed tomography volumetry and magnetic resonance elastography for major complications after liver resection

Evaluation of liver regeneration after hemi-hepatectomy by combining computed tomography and post-operative liver function

Background

Accurate evaluation of postoperative liver regeneration is essential to prevent postoperative liver failure.

Aims

To analyze the predictors that affect liver regeneration after hemi-hepatectomy.

Method

Patients who underwent hemi-hepatectomy in Hangzhou First People's Hospital and Hangzhou Shulan Hospital from January 2016 to December 2021 were enrolled in this study. The regeneration index (RI) was calculated by the following equation: RI = [(postoperative total liver volume {TLVpost} - future liver remnant volume {FLRV}/FLRV] × 100 %. Hepatic dysfunction was defined according to the "TBilpeak>7" standard, which was interpreted as (peak) total bilirubin (TBil) >7.0 mg/dL. Good liver regeneration was defined solely when the RI surpassed the median with hepatic dysfunction. Logistic regression analyses were performed to estimate prognostic factors affecting liver regeneration.

Result

A total of 153 patients were enrolled, with 33 in the benign group and 120 patients in the malignant group. In the entire study population, FLRV% [OR 4.087 (1.405-11.889), P = 0.010], international normalized ratio (INR) [OR 2.763 (95%CI, 1.008-7.577), P = 0.048] and TBil [OR 2.592 (95%CI, 1.177-5.710), P = 0.018] were independent prognostic factors associated with liver regeneration. In the benign group, only the computed tomography (CT) parameter FLRV% [OR, 11.700 (95%CI, 1.265-108.200), P = 0.030] predicted regeneration. In the malignant group, parenchymal hepatic resection rate (PHRR%) [OR 0.141 (95%CI, 0.040-0.499), P = 0.002] and TBil [OR 3.384 (95%CI, 1.377-8.319), P = 0.008] were independent prognostic factors.

Conclusion

FLRV%, PHRR%, TBil and INR were predictive factors associated with liver regeneration.

Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma

Liver resection is an effective therapeutic option for patients with hepatocellular carcinoma. However, posthepatectomy liver failure (PHLF) remains a major cause of hepatectomy-related mortality, and the accurate prediction of PHLF based on preoperative assessment of liver functional reserve is a critical issue. The definition of PHLF proposed by the International Study Group for Liver Surgery has gained acceptance as a standard grading criterion. Liver function can be estimated using a variety of parameters, including routine blood biochemical examinations, clinical scoring systems, dynamic liver function tests, liver stiffness and fibrosis markers, and imaging studies. The Child-Pugh score and model for end-stage liver disease scores are conventionally used for estimating liver decompensation, although the alternatively developed albumin-bilirubin score shows superior performance for predicting hepatic dysfunction. Indocyanine green clearance, a dynamic liver function test mostly used in Japan and other Asian countries, serves as a quantitative estimation of liver function reserve and helps determine indications for surgical procedures according to the estimated risk of PHLF. In an attempt to improve predictive accuracy, specific evaluation of liver fibrosis and portal hypertension has gained popularity, including liver stiffness measurements using ultrasonography or magnetic resonance elastography, as well as noninvasive fibrosis markers. Imaging modalities, including Tc-99m-labeled galactosyl serum albumin scintigraphy and gadolinium-enhanced magnetic resonance imaging, are used for preoperative evaluation in combination with liver volume. This review aims to provide an overview of the usefulness of current options for the preoperative assessment of liver function in predicting PHLF.

Comparison between Intravoxel Incoherent Motion and Splenic Volumetry to Predict Hepatic Fibrosis Staging in Preoperative Patients

Intravoxel incoherent motion (IVIM) and splenic volumetry (SV) for hepatic fibrosis (HF) prediction have been reported to be effective. Our purpose is to compare the HF prediction of IVIM and SV in 67 patients with pathologically staged HF. SV was divided by body surface area (BSA). IVIM indices, such as slow diffusion-coefficient related to molecular diffusion (D), fast diffusion-coefficient related to perfusion in microvessels (D*), apparent diffusion-coefficient (ADC), and perfusion related diffusion-fraction (f), were calculated by two observers (R1/R2). D (p = 0.718 for R1, p = 0.087 for R2) and D* (p = 0.513, p = 0.708, respectively) showed a poor correlation with HF. ADC (p = 0.034, p = 0.528, respectively) and f (p < 0.001, p = 0.007, respectively) decreased as HF progressed, whereas SV/BSA increased (p = 0.015 for R1). The AUCs of SV/BSA (0.649-0.698 for R1) were higher than those of f (0.575-0.683 for R1 + R2) for severe HF (≥F3-4 and ≥F4), although AUCs of f (0.705-0.790 for R1 + R2) were higher than those of SV/BSA (0.628 for R1) for mild or no HF (≤F0-1). No significant differences to identify HF were observed between IVIM and SV/BSA. SV/BSA allows a higher estimation for evaluating severe HF than IVIM. IVIM is more suitable than SV/BSA for the assessment of mild or no HF.

Clinical application of Magnetic resonance elastography in hepatocellular carcinoma: from diagnosis to prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a major public health problem worldwide. Liver fibrosis is closely correlated with liver functional reserve and the risk of HCC development. Meanwhile, malignant tumors generally have high cellularity compared to benign tumors, which results in increased stiffness. Magnetic resonance elastography (MRE) has emerged as a new non-invasive technique for assessing tissue stiffness with excellent diagnostic accuracy, not only for assessing liver fibrosis but also for measuring tumor stiffness. Recent studies provide new evidence that MRE may play an important role in the management of patients with HCC and show several novel clinical applications, such as predicting the development of HCC, differentiating between benign/malignant liver lesions (FLL) and HCC pathological grades, assessing treatment response, and predicting recurrence after treatment, although some findings are controversial. Therefore, we conducted this review to summarize these novel applications of MRE in HCC patients and also discuss their limitations and future advancement.

A proposed model on MR elastography for predicting postoperative major complications in patients with hepatocellular carcinoma

Objective

To develop a model for predicting post-operative major complications in patients with hepatocellular carcinoma (HCC).

Methods

In all, 186 consecutive patients with pre-operative MR elastography were included. Complications were categorised using Clavien‒Dindo classification, with major complications defined as ≥Grade 3. Liver-stiffness measurement (LSM) values were measured on elastogram. The indocyanine green clearance rate of liver remnant (ICG-Krem) was based on the results of CT volumetry, intraoperative data, and ICG-K value. For an easy application to the prediction model, the continuous variables were converted to categories. Moreover, logistic regression analysis and fivefold cross-validation were performed. The prediction model's discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC), and the calibration of the model was assessed by the Hosmer‒Lemeshow test.

Results

43 of 186 patients (23.1%) had major complications. The multivariate analysis demonstrated that LSM, albumin-bilirubin (ALBI) score, intraoperative blood loss, and ICG-Krem were significantly associated with major complications. The median AUC of the five validation subsets was 0.878. The Hosmer-Lemeshow test confirmed no evidence of inadequate fit (p = 0.13, 0.19, 0.59, 0.59, and 0.73) on the fivefold cross-validation. The prediction model for major complications was as follows: -2.876 + 2.912 [LSM (>5.3 kPa)]+1.538 [ALBI score (>-2.28)]+0.531 [Intraoperative blood loss (>860 ml)]+0.257 [ICG-Krem (<0.10)].

Conclusion

The proposed prediction model can be used to predict post-operative major complications in patients with HCC.

Advances in knowledge

The proposed prediction model can be used in routine clinical practice to identify post-operative major complications in patients with HCC and to strategise appropriate treatments of HCC.