A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition)

Background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in China. Surgical resection is the preferred treatment for HCC, but the postoperative recurrence and metastasis rates are high. Current evidence shows that microvascular invasion (MVI) is an independent risk factor for postoperative recurrence and metastasis, but there are still many controversies about the diagnosis, classification, prediction, and treatment of MVI worldwide.

Methods

Systematic literature reviews to identify knowledge gaps and support consensus statements and a modified Delphi method to develop evidence- and expert-based guidelines and finalization of the clinical consensus statements based on recommendations from a panel of experts.

Results

After many discussions and revisions, the Chinese Association of Liver Cancer of the Chinese Medical Doctor Association organized domestic experts in related fields to form the "Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition)" which included eight recommendations to better guide the prediction, diagnosis and treatment of HCC patients with MVI. The MVI pathological grading criteria as outlined in the "Guidelines for Pathological Diagnosis of Primary Liver Cancer" and the Eastern Hepatobiliary Surgery Hospital (EHBH) nomogram for predicting MVI are highly recommended.

Conclusions

We present an expert consensus on the diagnosis and treatment of MVI and potentially improve recurrence-free survival (RFS) and overall survival (OS) for HCC patients with MVI.

Imaging-Based Prediction of Ki-67 Expression in Hepatocellular Carcinoma: A Retrospective Study

AIM

This study aims to develop a non-invasive, preoperative predictive model for Ki-67 expression in HCC patients using enhanced computed tomography (CT) and clinical indicators to improve patient outcomes.

METHODS

This retrospective study analyzed 595 post-curative hepatectomy HCC patients. Patients were categorized into high (> 20%) and low (≤ 20%) Ki-67 expression groups based on cellular proliferation levels. Radiomic features were extracted from enhanced CT scans and combined with clinical parameters to develop a predictive model for Ki-67 expression.

RESULTS

Key clinical factors impacting Ki-67 expression in HCC included alpha-fetoprotein (AFP), non-smooth tumor margin, ill-defined pseudo-capsule, and peritumoral star node. From 1441 initially extracted radiomic features, 16 key features were selected using Lasso regression. These features were used to develop a radiomics model, which, when combined with clinical data, yielded an integrated predictive model with high accuracy. The combined model achieved an area under the curve (AUC) of 0.854 in the training group and 0.839 in the validation group. A nomogram based on this model was constructed, and its predictive accuracy was validated through calibration curves and decision curve analysis. A risk scorecard model was also constructed as a practical tool for clinicians to assess the risk level of high Ki-67 expression, facilitating personalized treatment planning. Survival analysis demonstrated significant differences in 3-year overall survival (OS) and progression-free survival (PFS) rates between patients with high and low Ki-67 expression, indicating the model's strong prognostic capability.

CONCLUSIONS

This study successfully developed a comprehensive model that integrates radiomic and clinical data for the preoperative prediction of Ki-67 expression in HCC patients.

A new model based on preoperative AFP, albumin, and tumor burden score for predicting microvascular invasion in early-stage HCC

Microscopic vascular invasion (MVI) has been demonstrated as a strong risk factor associated with tumor recurrence and poor overall survival among hepatocellular carcinoma (HCC) patients after resection, but the preoperative prediction of MVI is still challenging. We aimed to build and validate a novel model to predict MVI in the preoperative setting. We retrospectively collected 857 patients with Barcelona Clinic Liver Cancer (BCLC) stage 0 or A HCC who underwent primary resection at Kaohsiung Chang Gung Hospital between January 2001 and June 2016. The patients were randomized into derivation (n = 648) and validation groups (n = 209). Logistic regression analysis was used to screen out independent risk factors for MVI and further constructed a predictive model for MVI. Prediction performance was compared by the area under the receiver operating characteristic curve (AUC). The multivariable logistic regression analysis of the training cohort found that alpha-fetoprotein (AFP) ≥ 20 ng/mL (OR = 1.96, 95% CI: 1.41-2.73, P < 0.001), albumin < 3.5 g/dL (OR = 1.48, 95% CI: 1.06-2.05, P = 0.019) and tumor burden score (TBS) ≥ 8.6 (OR = 2.54, 95% CI: 1.49-4.35, P = 0.001) to be independent risk factors for MVI. The three factors were chosen to build a model for prediction of MVI. The AUC for the training and validation group was 0.619 (95% CI: 0.575-0.663) and 0.642 (95% CI: 0.562-0.722), respectively, and the calibration plot showed good performance of the prediction model, with a low mean absolute error at 0.01. In conclusion, the new model comprised AFP, albumin, and TBS that can predict risk of MVI for early-stage HCC.

Preoperative systemic inflammatory response index as a prognostic marker for distal cholangiocarcinoma after pancreatoduodenectomy

BACKGROUND

The relationship between preoperative inflammation status and tumorigenesis as well as tumor progression is widely acknowledged.

AIM

To assess the prognostic significance of preoperative inflammatory biomarkers in patients with distal cholangiocarcinoma (dCCA) who underwent pancreatoduodenectomy (PD).

METHODS

This single-center study included 216 patients with dCCA after PD between January 1, 2011, and December 31, 2022. The individuals were categorized into two sets based on their systemic inflammatory response index (SIRI) levels: A low SIRI group (SIRI < 1.5, n = 123) and a high SIRI group (SIRI ≥ 1.5, n = 93). Inflammatory biomarkers were evaluated for predictive accuracy using receiver operating characteristic curves. Both univariate and multivariate Cox proportional hazards analyses were performed to estimate SIRI for overall survival (OS) and recurrence-free survival (RFS).

RESULTS

The study included a total of 216 patients, with 58.3% being male and a mean age of 65.6 ± 9.6 years. 123 patients were in the low SIRI group and 93 were in the high SIRI group after PD for dCCA. SIRI had an area under the curve value of 0.674 for diagnosing dCCA, showing better performance than other inflammatory biomarkers. Multivariate analysis indicated that having a SIRI greater than 1.5 independently increased the risk of dCCA following PD, leading to lower OS [hazard ratios (HR) = 1.868, P = 0.006] and RFS (HR = 0.949, P < 0.001). Additionally, survival analysis indicated a significantly better prognosis for patients in the low SIRI group (P < 0.001).

CONCLUSION

It is determined that a high SIRI before surgery is a significant risk factor for dCCA after PD.

Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging

RATIONALE AND OBJECTIVES

To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI).

MATERIALS AND METHODS

62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f1 (fraction of restricted diffusion), f2 (fraction of hindered diffusion), f3 (fraction of free diffusion), and f1f2 (the multiply of f1 and f2) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy.

RESULTS

MVI-positive HCC showed significantly higher f1 and lower ADC values (ADC: 1.549 ± 0.228 ×10-3 vs 1.365 ± 0.239 ×10-3 mm2/s, P = .003; f1: 0.1633 ± 0.0341 vs 0.2221 ± 0.0491, P < .001). Tumor size and f1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f1. Nomogram-based model (area under ROC curve [AUC]= 0.856) yielded the best diagnostic accuracy followed by f1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f1 and nomogram model were significantly higher than that of ADC.

CONCLUSION

RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management.

Liver Segment Disposition of Hepatocellular Carcinoma Predicts Microvascular Invasion

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer morbidity and mortality. Findings of microvascular invasion (MVI) in patients with HCC have emerged as an important prognostic factor for poor survival after tumor resection.

Aim

This study evaluated the relation between MVI and HCC within various anatomical Couinaud's segments of the liver.

Method

A multicenter retrospective review of HCC records was conducted from 2012 to 2017. HCC cases were identified using ICD-9 and 10 codes 155, C22.0, and C22.8. HCC patients who underwent liver transplants were included in this study. Liver segment of the location of HCC was obtained from radiographic records, and MVI information was obtained from pathology reports. Segmental distributions of HCC in MVI versus non-MVI groups were compared using Wilcoxon rank sum tests. p value was set at <0.05.

Results

We analyzed 120 HCC patients who underwent liver transplantation. The mean age of our cohort was 57 years, and the most common etiology of liver disease was hepatitis C at 58.3%. The median HCC size was 3.1 cm, and MVI was present in 23.3% of the explanted specimens. MVI was 2 to 3 times significantly higher in patients with HCC affecting segments 2 and 3 and segments 4b and 5 (p = 0.01). Moreover, median survival was significantly lower in patients with MVI versus those without MVI (50 vs. 137 months, p < 0.05).

Conclusion

MVI was significantly higher in HCC tumors located in liver segments 2 and 3 and 4b and 5, and survival was lower in patients with MVI compared with those without.

A nomogram for preoperative prediction of microvascular invasion in ruptured hepatocellular carcinoma

BACKGROUND AND AIM

Microvascular invasion (MVI) is defined as the presence of micrometastatic cancer cell emboli in hepatic vessels, including small vessels, and at present, researchers believe that is an important factor for early postoperative recurrence and survival. Here, we developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured hepatocellular carcinoma (rHCC).

METHODS

We retrospectively collected data for 210 rHCC patients who underwent staged hepatectomy at Wuhan Tongji Hospital, and 91 patients who underwent staged hepatectomy at Zhongshan People's Hospital between January 2010 and March 2021. Then, the former was used as the training cohort and the latter was used as the validation cohort. Logistic regression was used to screen for variables associated with MVI, and these variables were used to construct nomograms. We used R software to assess the discrimination, calibration ability, as well as clinical efficacy of nomograms.

RESULTS

Multivariate logistic regression analysis identified four risk factors independently associated with MVI: max tumor length [odds ratio (OR) = 1.385; 95% confidence interval (CI), 1.072-1.790], number of tumors (OR = 2.182; 95% CI, 1.129-5.546), direct bilirubin (OR = 1.515; 95% CI, 1.189-1.930), and alpha-fetoprotein (cutoff = 400 ng/mL) (OR = 2.689; 95% CI, 3.395-13.547). Nomograms were built from the four variables and they were tested for discrimination and calibration, and the results were good.

CONCLUSION

We developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured HCC. This model can help clinicians identify patients at risk of MVI and make better treatment options.