Potential predictive factors for microvascular invasion in hepatocellular carcinoma classified within the Milan criteria

New predictors of microvascular invasion for small hepatocellular carcinoma ≤ 3 cm

BACKGROUND

Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm.

METHODS

We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort.

RESULTS

In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively.

CONCLUSIONS

Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography

PURPOSE

Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images.

METHODS

A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV).

RESULTS

Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917).

CONCLUSION

The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

A novel nomogram for predicting microvascular invasion in hepatocellular carcinoma

INTRODUCTION AND OBJECTIVES

In hepatocellular carcinoma (HCC), the prognosis of patients with microvascular invasion (MVI) is poor. Therefore, in this study, we established and evaluated the performance of a novel nomogram to predict MVI in patients with HCC.

MATERIALS AND METHODS

We retrospectively obtained clinical data of 497 patients with HCC who underwent hepatectomy at Liaoning Cancer Hospital from November 1, 2018, to November 4, 2021. The patients (n = 497) were randomized in a 7:3 ratio into the training cohort (TC, n = 349) and the validation cohort (VC, n = 148). We performed Least Absolute Shrinkage and Selection Operator (LASSO) and univariate as well as multivariate logistic regression analyses (ULRA, MRLA) on patients in the TC to identify factors independently predicting MVI.

RESULTS

Preoperative FIB-4, AFU, AFP levels, liver cirrhosis, and non-smooth tumor margin were independent risk factors for preoperative MVI prediction. The C-index of the TC, VC, and the entire cohort was 0.846, 0.786, and 0.829, respectively. The calibration curves demonstrated the outstanding agreement between predicted MVI incidences by our model and the actual MVI risk. Decision curve analysis (DCA) confirmed the significance of our predictive model in clinical settings. The Kaplan-Meier (KM) survival curve showed that the recurrence-free survival (RFS) and overall survival (OS) of patients in the high-MVI risk group were poor compared to those in the low-MVI risk group.

CONCLUSIONS

We constructed and evaluated the performance of the novel nomogram for predicting MVI risk. Our predictive model could adequately predict MVI risk and aid clinicians in selecting appropriate therapeutic strategies for patients.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.

The appropriate method of hepatectomy for hepatocellular carcinoma within University of California San Francisco (UCSF) criteria through neural network analysis

BACKGROUND

This study aimed to find effective treatments for the patient within UCSF criteria.

METHODS

This study enrolled 1006 patients meeting UCSF criteria, undergoing hepatic resection (HR), divided into two groups: single tumor group and multiple tumors group. We compared and analyzed the risk factors between these two groups' long-term outcomes, through log-rank test, cox proportional hazards model and using neural network analysis to identify the independent risk factors.

RESULTS

The 1-, 3-, and 5-year OS rates in single tumor were significantly higher than multiple tumors (95.0%, 73.2% and 52.3% versus 93.9%, 69.7% and 38.0%, respectively, p < 0.001). The 1-, 3- and 5-year RFS rates were 90.3%, 60.7%, and 40.1% in single tumor and 83.4%, 50.7% and 23.8% in multiple tumors, respectively (p < 0.001). And tumor type, anatomic resection and MVI were the independent risk factors for the patient within UCSF criteria. MVI was the most important risk factor affecting OS and RFS rates in neural network analysis. The method of hepatic resection and the number of tumors were also affected OS and RFS rates.

CONCLUSION

The anatomic resection should be applied to the patient within UCSF criteria, especially for the patient was in single tumor with MVI-negative.

Preoperative prediction model of microvascular invasion in patients with hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC.

METHODS

A total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Logistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility.

CONCLUSION

The nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions.

Preoperative determination of pathological grades of primary single HCC: development and validation of a scoring model

PURPOSE

This study aimed to establish a reliable diagnostic score model for the preoperative determination of pathological grade in HCC based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI and biochemical indicators.

METHODS

In this retrospective study, we analyzed 139 patients with HCC who underwent Gd-EOB-DTPA MRI between 2014 and 2020, including an establishment cohort of 76 patients and a validation cohort of 63 patients. Based on the imaging features demonstrated on Gd-EOB-DTPA MRI images and biochemical indicators of the establishment cohort, a scoring model based on logistic regression was developed, and compared with postoperative pathological findings in terms of effective determination of pathological grade. The validity of the scoring model was assessed by ROC curves and an independent external validation cohort.

RESULTS

Three parameters related to pathological grades were identified, including maximum diameter of the tumor, peritumoral hypointensity in the hepatobiliary phase, and [alkaline phosphatase (U/L) + gamma glutamyl transpeptidase (U/L)]/ lymphocyte count (× 10⁹/L) (AGLR) ratios. Based on these three parameters, a scoring model was developed. ROC curve showed that a score of > 5 was set as the threshold for determining pathological grades with accuracy, sensitivity, specificity, PPV, and NPV of 89.5%, 75.0%, 95.1%, 85.7%, and 90.7%, respectively.

CONCLUSION

The study provided the groundwork for a promising and easily implementable scoring model for preoperative determination of HCC pathological grades, for which further validation should be pursued.

A Glycoform-Resolved Dual-Modal Ratiometric Immunoassay Improves the Diagnostic Precision for Hepatocellular Carcinoma

The glycosylation pattern of alpha fetoprotein (AFP) paves the basis for precise early diagnosis of hepatocellular carcinoma (HCC). However, existing analytical methods ignore the contribution of terminal sialic acid, which has been reported to be highly connected with HCC. Besides, the development of diagnostic assays is severely hindered by the preparation of anti-glycans antibodies. Molecularly imprinted polymers (MIPs), as synthetic antibody mimics, provide unique strengths to address these issues. Herein, we report a MIPs-based dual-modal ratiometric immunoassay for precise HCC diagnosis. Using a "pit one against ten" MIP to recognize a subset of glycans containing sialic acid and/or core fucose, we demonstrated our assay exhibited improved precision as compared with ELISA. This assay provided not only a glycoform-resolved method for precise HCC diagnosis, but also a new paradigm for developing antibody mimics via molecular imprinting towards challenging biomedical applications.

A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma

Purpose:

The indocyanine green retention rate at 15 min (ICG-R15) is of great importance in the accurate assessment of hepatic functional reserve for safe hepatic resection. To assist clinicians to evaluate hepatic functional reserve in medical institutions that lack expensive equipment, we aimed to explore a novel approach to predict ICG-R15 based on CT images and clinical data in patients with hepatocellular carcinoma (HCC).

Methods

In this retrospective study, 350 eligible patients were enrolled and randomly assigned to the training cohort (245 patients) and test cohort (105 patients). Radiomics features and clinical factors were analyzed to pick out the key variables, and based on which, we developed the random forest regression, extreme gradient boosting regression (XGBR), and artificial neural network models for predicting ICG-R15, respectively. Pearson's correlation coefficient (R) was adopted to evaluate the performance of the models.

Results

We extracted 660 CT image features in total from each patient. Fourteen variables significantly associated with ICG-R15 were picked out for model development. Compared to the other two models, the XGBR achieved the best performance in predicting ICG-R15, with a mean difference of 1.59% (median, 1.53%) and an R-value of 0.90. Delong test result showed no significant difference in the area under the receiver operating characteristic (AUROCs) for predicting post hepatectomy liver failure between actual and estimated ICG-R15.

Conclusion

The proposed approach that incorporates the optimal radiomics features and clinical factors can allow for individualized prediction of ICG-R15 value of patients with HCC, regardless of the specific equipment and detection reagent (NO. ChiCTR2100053042; URL, http://www.chictr.org.cn).

Using Systemic Inflammatory Markers to Predict Microvascular Invasion Before Surgery in Patients With Hepatocellular Carcinoma

Background

Mounting studies reveal the relationship between inflammatory markers and post-therapy prognosis. Yet, the role of the systemic inflammatory indices in preoperative microvascular invasion (MVI) prediction for hepatocellular carcinoma (HCC) remains unclear.

Patients and Methods

In this study, data of 1,058 cases of patients with HCC treated in the First Affiliated Hospital of Sun Yat-sen University from February 2002 to May 2018 were collected. Inflammatory factors related to MVI diagnosis in patients with HCC were selected by least absolute shrinkage and selection operator (LASSO) regression analysis and were integrated into an “Inflammatory Score.” A prognostic nomogram model was established by combining the inflammatory score and other independent factors determined by multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficacy of the model.

Results

Sixteen inflammatory factors, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, etc., were selected by LASSO regression analysis to establish an inflammatory score. Multivariate logistic regression analysis showed that inflammatory score (OR = 2.14, 95% CI: 1.63–2.88, p < 0.001), alpha fetoprotein (OR = 2.02, 95% CI: 1.46–2.82, p < 0.001), and tumor size (OR = 2.37, 95% CI: 1.70–3.30, p < 0.001) were independent factors for MVI. These three factors were then used to establish a nomogram for MVI prediction. The AUC for the training and validation group was 0.72 (95% CI: 0.68–0.76) and 0.72 (95% CI: 0.66–0.78), respectively.

Conclusion

These findings indicated that the model drawn in this study has a high predictive value which is capable to assist the diagnosis of MVI in patients with HCC.

The antitumour effect of galangin and luteolin with doxorubicin on chemically induced hepatocellular carcinoma in rats

Hepatocellular carcinoma (HCC), a highly malignant tumour with very high morbidity and mortality, remains the second cause of cancer-related deaths worldwide. Galangin is a naturally occurring flavonoid extracted from the propolis and root of Alpinia officinarum, which possesses antitumour efficacy, which has resulted in an increase in interest in related research. Additionally, galangin inhibits cell proliferation and induces apoptosis in several human malignancies. On the other hand, luteolin, a naturally occurring flavonoid found in a variety of edible plants, augments cytotoxicity in different cancer cells through the inhibition of cell-survival pathways and activation of apoptosis. Moreover, luteolin blocks the activity of anti-apoptotic Bcl-2 family members. The present study aimed to assess the antitumour effect of galangin and luteolin in combination and the antitumour effect of a combination of galangin and luteolin together with doxorubicin (DOX) in a chemically induced HCC rat model. Our analyses demonstrated that the combination treatment with galangin, luteolin, and DOX showed the greatest antineoplastic activity against HCC, which was observed by significant decreases in the levels of HCC markers, including serum α-fetoprotein-L3, and hepatic tissue expression of both glypican 3 and heat shock proteins. On the other hand, the hepatic tissue expression of caspase-3 was significantly increased. These results suggest that combination treatment with galangin and luteolin is a promising candidate for clinical use in HCC chemotherapy, especially when used in combination with DOX.

Risk Score Model for Microvascular Invasion in Hepatocellular Carcinoma: The Role of Tumor Burden and Alpha-Fetoprotein

Simple Summary

Microvascular invasion (MVI) is the most consistently reported risk factor for recurrence after curative treatment in hepatocellular carcinoma (HCC), but the preoperative prediction of MVI is still challenging. We retrospectively collected 1153 patients who underwent liver resection for HCC, and our multivariate analysis revealed preoperative total tumor volume (TTV) and alpha-fetoprotein (AFP) to be independent risk factors for MVI. We used both factors to build a risk score model that is easy to calculate and objective, with minimal user bias. The preoperative prediction of MVI can guide the treatment plan of HCC, including surgical planning, criteria for transplantation, and adjuvant or neoadjuvant therapy. Our risk score model is easily and widely applicable with moderate performance, which optimizes clinical practice and helps study design in the future.

Abstract

Microvascular invasion (MVI) is a significant risk factor for the recurrence of hepatocellular carcinoma, but it is a histological feature that needs to be confirmed after hepatectomy or liver transplantation. The preoperative prediction of MVI can optimize the treatment plan of HCC, but an easy and widely applicable model is still lacking. The aim of our study was to predict the risk of MVI using objective preoperative factors. We retrospectively collected 1153 patients who underwent liver resection for HCC, and MVI was found to be associated with significantly poor disease-free survival. The patients were randomly split in a 3:1 ratio into training (n = 864) and validation (n = 289) datasets. The multivariate analysis of the training dataset found preoperative total tumor volume (TTV) and alpha-fetoprotein (AFP) to be independent risk factors for MVI. We built a risk score model with cutoff points of TTV at 30, 60, and 300 cm³ and AFP at 160 and 2000 ng/mL, and the model stratified the risk of MVI into low risk (14.1%), intermediate risk (36.4%), and high risk (60.5%). The validation of the risk score model with the validation dataset showed moderate performance (the concordance statistic: 0.731). The model comprised simple and objective preoperative factors with good applicability, which can help to guide treatment plans for HCC and future study design.

Microvascular invasion may be the determining factor in selecting TACE as the initial treatment in patients with hepatocellular carcinoma

ABSTRACT

The aim of this study was to investigate factors affecting tumor necrosis with transcatheter arterial chemoembolization (TACE). Factors associated with early hepatocellular carcinoma recurrence after curative hepatectomy were also evaluated.Data of 51 patients who underwent surgery after a single session of TACE at a single university hospital were retrospectively analyzed. Factors that might affect tumor necrosis were determined by evaluating the TACE approach and by analyzing computed tomography and TACE findings, pathologic reports, and laboratory findings.In univariate analysis, microvascular invasion (MVI), radiological capsule appearance on the computed tomography, chronic hepatitis B, diabetes mellitus and serum albumin, MVI were significantly associated with tumor necrosis by TACE (P < .02). In multivariate analysis, MVI was the only statistically significant factor in TACE-induced tumor necrosis (P = .001). In univariate and multivariate analysis, MVI was the strongest factor for recurrence-free survival rate within 2 years (P = .008, P = .002).MVI could be a crucial factor in determining TACE as an initial treatment for hepatocellular carcinoma. MVI is also a strong indicator of recurrence within 2 years after curative hepatic resection.

The effect of bile duct tumor thrombus on the long-term prognosis of hepatocellular carcinoma patients after liver resection: a systematic review and meta-analysis

Background

The effect of bile duct tumor thrombus (BDTT) on the postoperative long-term prognosis of hepatocellular carcinoma (HCC) patients is still under debate.

Methods

The PubMed, Embase, Cochrane Library, Web of Science databases were systematically searched to collect the clinicopathologic characteristics, perioperative indices, and postoperative survival outcomes in the BDTT and non-BDTT groups of HCC patients from inception to February 1, 2020. The study outcomes were extracted by two independent investigators.

Results

A total of 15 studies involving 6,484 patients were included. The meta-analysis revealed that the levels of serum total bilirubin and alkaline phosphatase were notably higher in patients with HCC and BDTT than those without BDTT. Meanwhile, HCC patients with BDTT had more aggressive biological characteristics, such as poor tumor differentiation, macrovascular invasion, and lymph node metastasis, as compared to patients without BDTT. The 1-year [odds ratio (OR) 0.39, 95% confidence interval (CI): 0.31–0.48, P<0.01], 3-year (OR 0.33, 95% CI: 0.22–0.51, P<0.01) and 5-year overall survival (OS) rates (OR 0.31, 95% CI: 0.20–0.49, P<0.01) of the BDTT group were significantly worse than those of the non-BDTT group. The hazard ratio of HCC with BDTT was 4.27 (95% CI: 3.47–5.26, P<0.01) within 5 years after hepatectomy.

Conclusions

HCC patients with BDTT had worse OS compared to patients free of BDTT after surgery. BDTT may be a potential prognostic factor for HCC patients.

Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI

BACKGROUND

Microvascular invasion (MVI) is implicated in the poor prognosis of hepatocellular carcinoma (HCC). Presurgical stratifying schemes have been proposed for HCC-MVI but lack external validation.

PURPOSE

To perform external validation and comparison of four presurgical stratifying schemes for the prediction of MVI using gadoxetic acid-based MRI in a cohort of HCC patients.

STUDY TYPE

Retrospective.

SUBJECTS

Included were 183 surgically resected HCCs from patients who underwent pretreatment MRI.

FIELD STRENGTH/SEQUENCE

This includes 1.5-3.0 T with T₂ , T₁ , diffusion-weighted imaging (DWI), and dynamic gadoxetic acid contrast-enhancement imaging sequences.

ASSESSMENT

A two-trait predictor of venous invasion (TTPVI), Lei model, Lee model, and Xu model were compared. We relied on preoperative characteristics and imaging findings via four independent radiologists who were blinded to histologic results, as required by the tested tools.

STATISTICAL TEST

Tests of accuracy between predicted and observed HCC-MVI rates using receiver operating characteristic (ROC) curve and decision curve analysis. The intraclass correlation coefficient (ICC) and Cronbach's alpha statistics were used to evaluate reproducibility.

RESULTS

HCC-MVI was identified in 52 patients (28.4%). The average ROC curves (AUCs) for HCC-MVI predictions were 0.709-0.880, 0.714-0.828, and 0.588-0.750 for the Xu model, Lei model, and Lee model, respectively. The rates of accuracy were 60.7-81.4%, 69.9-75.9%, and 65.6-73.8%, respectively. Decision curve analyses indicated a higher benefit for the Xu and Lei models compared to the Lee model. The ICC and Cronbach's alpha index were highest in the Lei model (0.896/0.943), followed by the Xu model (0.882/0.804), and the Lee model (0.769/0.715). The TTPVI resulted in a Cronbach's alpha index of 0.606 with a sensitivity of 34.6-61.5% and a specificity of 76.3-91.6%.

DATA CONCLUSION

Stratifying schemes relying on gadoxetic acid-enhanced MRI provide an additional insight into the presence of preoperative MVI. The Xu model outperformed the other models in terms of accuracy when performed by an experienced radiologist. Conversely, the Lei model outperformed the other models in terms of reproducibility.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:433-447.

Effect of prophylactic transcatheter arterial chemoembolization on hepatocellular carcinoma with microvascular invasion after R0 resection. A case-control study

BACKGROUND

Transcatheter arterial chemoembolization (TACE) is thought to prevent recurrence of hepatocellular carcinoma (HCC), but its efficacy is a matter of controversy.

OBJECTIVES

We investigated the effect of preventive TACE on the tumor, nodes, metastasis (TNM) classification in cases of stage II HCC (T2N0M0) after R0 resection.

DESIGN AND SETTING

Case-control study conducted in a tertiary-level public hospital.

METHODS

We analyzed recurrence rates and mortality rates over time for 250 consecutive cases of HCC in TNM classification cases of stage II HCC (T2N0M0) after R0 resection. These cases were divided into patients who underwent TACE (TACE+) and presented microvascular invasion (MVI+; n = 80); TACE+ but did not present MVI (MIV-; n = 100); MVI+ but did not undergo TACE (TACE-, n = 30); and TACE-/MVI- (n = 40).

RESULTS

MVI+ patients in the TACE+ group had significantly lower recurrence rates and mortality rates at one, two and three years than those in the TACE- group (all P < 0.05). Among MVI- patients, the TACE+ group did not have significantly lower recurrence rates and mortality rates at one, two and three years than the TACE- group (all P > 0.05). Regardless of whether TACE was performed or not, MVI- patients had significantly lower recurrence rates and mortality rates at two and three years after their procedures than did MVI+ patients (all P < 0.05).

CONCLUSION

Recurrence rates and mortality rates for MVI+ patients were significantly higher than for MVI- patients, beyond the first year after TACE. Postoperative adjuvant TACE may be beneficial for HCC patients with MVI.

Correlation of postoperative splenic volume increase with prognosis of hepatocellular carcinoma after curative hepatectomy

Background

Previous studies have reported a close connection between the spleen and hepatic tumours. We investigated the prognostic value of postoperative splenic volume increase (PSVI) in patients with hepatocellular carcinoma after curative hepatectomy.

Methods

This was a retrospective study of adult patients with hepatocellular carcinoma who underwent hepatectomy between January 2007 and May 2013. We categorized patients into 2 groups according to the cut-off value of the receiver operating characteristic curve: group A (PSVI < 19.0%) and group B (PSVI ≥ 19.0%). We compared the clinicopathological data, overall survival and disease-free survival between the 2 groups. We performed univariate and multivariate analyses to identify factors associated with disease-free and overall survival.

Results

There were 275 patients in group A and 196 patients in group B. The 1-, 3- and 5-year overall survival rates were 98.9%, 74.9% and 63.6%, respectively, for patients in group A, and 97.4%, 65.3% and 49.8%, respectively, for patients in group B (p = 0.004). The corresponding disease-free survival rates were 69.5%, 48.0% and 40.3%, and 58.1%, 36.5%, and 29.8% (p = 0.01). On multivariate analysis, PSVI was an independent predictor of overall (p = 0.01) and disease-free (p = 0.03) survival.

Conclusion

Postoperative splenic volume increase correlates with poor prognosis of patients with hepatocellular carcinoma after curative hepatectomy.

Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma

The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.

Electrochemical Assay of the Alpha Fetoprotein-L3 Isoform Ratio To Improve the Diagnostic Accuracy of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is now the major malignant disease with high morbidity and mortality, which seriously endangers human lives and health. Alpha fetoprotein (AFP) assay is a commonly used serological biomarker for clinical diagnosis of HCC, but it lacks specificity. Analysis of its isoform AFP-L3, especially the AFP-L3 ratio in total AFP (AFP-L3%), can significantly improve the specificity for HCC identification. Herein, an electrochemical approach has been first proposed for simple, accurate, and fast determination of AFP-L3% in clinical samples. On the basis of two independent electrochemical signals generated from the synthesized nanoparticles, 4-mercaptophenylboronic acid (MPA)-functionalized copper nanoparticles (MPA-CuNPs) and the Lens culinaris agglutinin (LCA)-functionalized silver nanoparticles (LCA-AgNPs), simultaneous quantification of the AFP-L3 and total AFP in serum sample has been achieved, thus achieving directly the electrochemical assay of AFP-L3%. To be noted, both the assay time and the assay procedure have been significantly compressed when compared to that of available techniques in clinical use. Therefore, with the integration of electrochemical techniques, this new approach for AFP-L3% analysis would be promising for the accurate diagnosis of HCC.