This study aimed to evaluate the value of simultaneous multislice diffusion kurtosis imaging (SMS-DKI) for predicting early recurrence (within 2 years) in hepatocellular carcinoma (HCC) and to develop a predictive model.

This prospective study included 67 HCC patients who underwent SMS-DKI on a 3-T MRI between June 2021 and January 2023. Diffusion parameters, including the apparent diffusion coefficient (ADC), SMS-mean kurtosis (SMS-MK), and SMS-mean diffusivity (SMS-MD), along with radiological features, were analyzed. Logistic regression models were used to predict early recurrence, internally validated using 10-fold cross-validation, and assessed using AUC, calibration curves, and decision curve analysis (DCA).

Among 67 patients (58 males; mean age, 53.5 ± 9.9 years), 30 (44.8%) experienced early recurrence. The early recurrence had significantly lower ADC (1.12 vs 1.22 × 10-3 mm2/s) and SMS-MD (1.45 vs 1.70 × 10-3 mm2/s), and higher SMS-MK (0.91 vs 0.75). SMS-MK showed the highest AUC (0.90, 95% CI: 0.80-0.96). Multivariate analysis identified SMS-MK (OR = 3.43 [1.31-8.89]), tumor size (OR = 4.22 [1.58-7.76]), non-smooth tumor margin (OR = 2.68 [1.58-7.96]), and complete capsule (OR = 0.22 [0.02-0.79]) as independent predictors of early recurrence. Based on these four parameters, the final model achieved an AUC of 0.94 (95% CI: 0.88-1.00). Calibration curves and DCA confirmed clinical utility.

SMS-DKI enhances early recurrence prediction in HCC. The predictive model, incorporating SMS-MK, tumor size, and key radiological features, demonstrated good prognostic value.

Question Can SMS-DKI predict HCC early recurrence within 2 years post-surgery? Findings Higher SMS-MK, larger tumor size, non-smooth margins, and incomplete capsule predict HCC early recurrence (model AUC = 0.94). Clinical relevance Integrating preoperative SMS-DKI biomarkers (SMS-MK) with tumor size and capsule status stratifies early HCC recurrence risk, guiding surgical planning and postoperative management.

To develop and validate a comprehensive model for predicting postoperative early recurrence of hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI.

239 patients with HCC who underwent curative surgical resection were recruited from two centers between April 2017 and December 2022. Radiomics features were extracted from the region of interest (ROI) on preoperative Gd-EOB-DTPA-enhanced MR images, and consistency analysis was performed to select stable radiomics features. Significant variables in the univariate and multivariate logistic regression analysis were included in clinical-radiologic model. Nomograms were constructed by combining the best performing radiologic and clinical-radiologic characteristics. Recurrence-free survival (RFS) comparisons were conducted using the log-rank test based on high versus low model-derived scores.

The radiomics model based on multiple phases MR outperformed all other radiomics models and had the best discrimination for early recurrence, with AUC of 0.799 and 0.743 in the training and validation sets, respectively. In the entire cohort, high-risk patients exhibited significantly lower RFS compared to low-risk patients.

The nomogram integrating Gd-EOB-DTPA enhanced MRI radiomics features and clinical-radiologic characteristics demonstrate superior predictive performance with postoperative early recurrence in patients with HCC. The model can identify patients at high risk and provide support for individualized treatment planning.

To predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems.

One hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems.

Six variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models.

The preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.

This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.

We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.

Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.

The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

The prognosis of hepatocellular carcinoma (HCC) is still poor largely due to the high incidence of recurrence. We aimed to develop and validate predictive models of early postoperative recurrence for HCC using clinical and gadoxetic acid-enhanced magnetic resonance (MR) imaging-based findings.

In this retrospective case-control study, 209 HCC patients, who underwent gadoxetic acid-enhanced MR imaging before curative-intent resection, were enrolled. Boruta algorithm and backward stepwise selection with Akaike information criterion (AIC) were used for variables selection Random forest, Gradient-Boosted decision tree and logistic regression model analysis were used for model development. The area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis were used to evaluate model's performance.

One random forest model with Boruta algorithm (RF-Boruta) was developed consisting of preoperative serum ALT and AFP levels and six MRI findings, while preoperative serum AST and AFP levels and four MRI findings were included in one logistic regression model with backward stepwise selection method (Logistic-AIC).The two predictive models demonstrated good discrimination performance in both the training set (RF-Boruta: AUC, 0.820; Logistic-AIC: AUC, 0.853), internal validation set (RF-Boruta: AUC, 0.857, Logistic-AIC: AUC, 0.812) and external validation set(RF-Boruta: AUC, 0.805, Logistic-AIC: AUC, 0.789). Besides, in both the internal validation and external validation sets, the RF-Boruta model outperformed Barcelona Clinic Liver Cancer (BCLC) stage (p < 0.05).

The RF-Boruta and Logistic-AIC models with good prediction performance for early postoperative recurrence may lead to optimal and comprehensive treatment approaches, and further improve the prognosis of HCC after resection.

To establish a risk score integrating preoperative gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) and clinical parameters to predict recurrence after hepatectomy for patients with hepatocellular carcinoma (HCC) and to compare its performance with that of a postoperative score and four clinical staging systems.

Consecutive patients with surgically confirmed HCC who underwent preoperative EOB-MRI between July 2015 and November 2020 were retrospectively included. Two recurrence risk scores, one incorporating only preoperative variables and the other incorporating all preoperative and postoperative variables, were constructed via Cox regression models.

A total of 214 patients (derivation set, n = 150; test set, n = 64) were included. Six preoperative variables, namely tumor number, infiltrative appearance, corona enhancement, alpha-fetoprotein (AFP) level, aspartate aminotransferase (AST) level, and sex, were independently associated with recurrence. After adding postoperative features, microvascular invasion and tumor differentiation were additional significant variables in lieu of corona enhancement and AFP level. Using the above variables, the preoperative score achieved a C-index of 0.741 on the test set, which was comparable with that of the postoperative score (0.729; p = 0.235). The preoperative score yielded a larger time-dependent area under the receiver operating characteristic curve at 1 year (0.844) than three existing systems (0.734-0.742; p < 0.05 for all). Furthermore, the preoperative score stratified patients into two prognostically distinct risk strata with low and high risks of recurrence (p < 0.001).

The preoperative score integrating EOB-MRI features, AFP and AST levels, and sex improves recurrence risk estimation in HCC.

• The preoperative risk score incorporating three EOB-MRI findings, AFP and AST levels, and sex achieved comparable performance with that of the postoperative score for predicting recurrence after hepatectomy in patients with HCC. • Two risk strata with low and high risks of recurrence were obtained based on the preoperative score. • The preoperative score may help tailor pretreatment decision-making and facilitate candidate selection for adjuvant clinical trials.

To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5 cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements.

One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical-radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical-radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance.

Cirrhosis, age, and albumin were significantly associated with ER in the clinical-radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical-radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively.

The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical-radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5 cm.

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

Patients with hepatocellular carcinoma (HCC) receiving different treatments might have specific prognostic factors that can be captured in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced magnetic resonance imaging (GA-MRI). We aimed to identify the clinical findings and HBP features with prognostic value in patients with HCC.

In this retrospective, single-institution study, we included patients with Barcelona Clinic Liver Cancer very early/early stage HCC who underwent GA-MRI before treatment. After performing propensity score matching, 183 patients received the following treatments: resection, radiofrequency ablation (RFA), and transarterial chemoembolization (TACE) (n = 61 for each). Cox regression models were used to identify clinical factors and HBP features associated with disease-free survival (DFS) and overall survival (OS).

In the resection group, large tumor size was associated with poor DFS (hazard ratio [HR] 4.159 per centimeter; 95% confidence interval [CI], 1.669-10.365) and poor OS (HR 8.498 per centimeter; 95% CI, 1.072-67.338). In the RFA group, satellite nodules on HBP images were associated with poor DFS (HR 5.037; 95% CI, 1.061-23.903) and poor OS (HR 9.398; 95% CI, 1.480-59.668). Peritumoral hypointensity on HBP images was also associated with poor OS (HR 13.062; 95% CI, 1.627-104.840). In addition, serum albumin levels and the prothrombin time-international normalized ratio were associated with DFS and/or OS. Finally, in the TACE group, no variables were associated with DFS/OS.

Different HBP features and clinical factors were associated with DFS/OS among patients with HCC receiving different treatments.

• In patients who underwent resection for HCC, a large tumor size on HBP images was associated with poor disease-free survival and overall survival. • In the RFA group, satellite nodules and peritumoral hypointensity on HBP images, along with decreased serum albumin levels and PT-INR, were associated with poor disease-free survival and/or overall survival. • In the TACE group, no clinical or HBP imaging features were associated with disease-free survival or overall survival.

To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters to predict tumor recurrence after hepatectomy in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

One hundred and fifty-seven patients (mean age 52.54 ± 11.32 years, 87% male) with surgically and pathologically confirmed HCC were included. Regions of interests were drawn including the tumors by two independent radiologists. ADC and IVIM-derived parameters (true diffusion coefficient [D]; pseudodiffusion coefficient [D*]; pseudodiffusion fraction [f]) were obtained preoperatively. The Cox proportional hazards model was used to analyze the predictors associated with tumor recurrence after hepatectomy.

Forty-seven of 157 (29.9%) patients experienced tumor recurrence. The multivariate Cox proportional hazards model revealed that a D value < 0.985 × 10-3 mm2/s (hazard ratio (HR), 0.190; p = 0.023) was a risk factor for tumor recurrence. Additional risk factors included younger age (HR, 0.328; p = 0.034) and higher serum alpha-fetoprotein (AFP) level (HR, 2.079; p = 0.013). Further, receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of the obtained Cox regression model improved from 0.68 for the combination of AFP and age alone to 0.724 for the combination of D value, AFP, and age.

The D value derived from the IVIM model is a potential biomarker for the preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

• The recurrence rate of HCC after hepatectomy was higher in patients with ADC, D, and f values that were lower than the optimal cutoff values. • The optimal cutoff values of ADC, D, D*, and f for predicting recurrence in HBV associated HCC were 0.858 × 10-3 mm2/s, 0.985 × 10-3 mm2/s, 12.5 × 10-3 mm2/s, and 23.4%, respectively. • The D value derived from IVIM diffusion-weighted imaging may be a useful biomarker for preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

To evaluate the value of magnetic resonance imaging (MRI) features, including liver stiffness measured by magnetic resonance elastography (MRE) and the presence of hepatobiliary phase (HBP) hypointense nodule without arterial phase hyperenhancement (APHE), for predicting late recurrence (>1 year) after surgery for hepatocellular carcinoma (HCC).

This retrospective study included 124 consecutive patients who had undergone surgery for HCC and preoperative MRI. After excluding patients with early recurrence within 1 year after surgery, 89 patients were analysed. Preoperative MRI images were reviewed by a radiologist to record imaging findings, including (1) liver stiffness by MRE, (2) size of the HCCs, (3) number of HCCs, and (4) presence of HBP hypointense nodule without APHE. Pathological findings included tumour grade, vascular/biliary/capsule invasion, and fibrosis stage of the liver. Considering imaging/pathological findings and patients' characteristics as dependent variables, Cox proportional hazards model analysis was performed to identify independent factors associated with late recurrence after surgery.

The median follow-up period was 37.3 months. During follow-up, 29 patients (32.5%) developed late recurrence after surgery. In multivariate analysis, underlying liver disease (viral hepatitis) and presence of HBP hypointense nodules without APHE (p=0.010 and 0.033, respectively) were independently associated with disease-free survival (DFS). Kaplan-Meier analysis revealed that patients with HBP hypointense nodules without APHE had a significantly lower DFS rate than those without the nodule (39.2% versus 74.1% at 3 years after surgery, p=0.008).

The presence of HBP hypointense nodules without APHE was an indicator of late recurrence after surgery for HCC.

Our purpose was to demonstrate the prognostic significance of T₁ mapping on gadoxetic acid-enhanced MR imaging in prediction of recurrence of single HCC after hepatectomy.

One hundred and seven patients with single nodular HCC (≤3 cm) who underwent preoperative gadoxetic acid-enhanced MRI were included in the study. T₁ mapping with syngo MapIt was obtained on a 1.5 T scanner. Radiological features and reduction rate of T₁ relaxation time (Δ%) of tumors were assessed by two radiologists. Cumulative recurrence rates were compared between groups of low and high reduction rate of T₁ relaxation time. A further classified cumulative recurrence rate of the overall cohort was based on the numbers of independent predictive factors.

Reduction rate of T₁ relaxation time (P = 0.001) and non-hypervascular hypointense nodules (P = 0.042) in preoperative gadoxetic acid-enhanced MRI were independently related to recurrence of HCC after hepatectomy. Patients of lower reduction rates group had higher cumulative recurrence rates (P < 0.0001) than patients of higher reduction rates group. A combination of the two risk factors in patients with single HCC had significantly higher recurrence rates compared to those with either or none of the two risk factors.

Reduction rate of T₁ relaxation time combined with non-hypervascular hypointense nodules can be reliable biomarkers in the preoperative prediction of recurrence of HCC after hepatectomy.

Deregulation of methionine adenosyltransferase (MAT) is involved in hepatocarcinogenesis. This study aimed to investigate the prognostic implications of the level of histological MAT1A and MAT2A in patients with resected hepatocellular carcinoma (HCC).

A total of 210 patients with HCC who underwent curative resection between 2004 and 2011 were included. The levels of MAT proteins were immunohistochemically measured.

MAT1A and MAT2A were over-expressed in 134 (63.8%) and 124 (59.1%) of the 210 tumor tissues, respectively. Up-regulation of tumoral MAT1A was independently associated with male gender, and inversely related to tumors >5 cm (adjusted odds ratios [OR] 2.59, P = 0.008, and OR 0.44, P = 0.012, respectively). Enhanced MAT2A expression was significantly related to age ≥60 years and serum AFP >200 ng/mL (OR 0.51, P = 0.030; and OR 2.65, P = 0.003; respectively). Tumoral MAT2A over-expression independently predicted an increased rate of recurrence within 1 year after hepatectomy (adjusted hazard ratio [HR] 2.45, P = 0.012), but that was not the case for MAT1A expression (HR 0.90, P = 0.744). High MAT2A was also an independent predictor of early recurrence (HR 2.54, P = 0.034) in the subset of patients without microvascular invasion (n = 155).

Over-expression of MAT2A in HCC may be a useful biomarker for predicting and monitoring tumor recurrence, especially early after hepatic resection.