Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study

Development and validation of a nomogram for predicting microvascular invasion and evaluating the efficacy of postoperative adjuvant transarterial chemoembolization

Background and aim

Accurately predicting microvascular invasion (MVI) before surgery is beneficial for surgical decision-making, and some high-risk hepatocellular carcinoma (HCC) patients may benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). The purpose of this study was to develop and validate a novel nomogram for predicting MVI and assessing the survival benefits of selectively receiving PA-TACE in HCC patients.

Methods

The 1372 HCC patients who underwent hepatectomy at four medical institutions were randomly divided into training and validation datasets according to a 7:3 ratio. We developed and validated a nomogram for predicting MVI using preoperative clinical data and further evaluated the survival benefits of selective PA-TACE in different risk subgroups.

Results

The nomogram for predicting MVI integrated alpha-fetoprotein, tumor diameter, tumor number, and tumor margin, with an area under the curve of 0.724, which was greater than that of any single predictive factor. The calibration curve, decision curve, and clinical impact curve demonstrated that the nomogram had strong predictive performance. Risk stratification based on the nomogram revealed that patients in the low-risk group did not achieve better DFS and OS with PA-TACE (all p > 0.05), while patients in the medium-to-high risk groups could benefit from higher DFS (Medium-risk, p = 0.039; High-risk, p = 0.027) and OS (Medium-risk, p = 0.001; High-risk, p = 0.019) with PA-TACE.

Conclusions

The nomogram predicting MVI demonstrated strong predictive performance, and its risk stratification aided in identifying different subgroups of HCC patients who may benefit from PA-TACE with improved survival outcomes.

A simple model to predict early recurrence of hepatocellular carcinoma after liver resection

PURPOSE

Multiple studies have reported models for predicting early recurrence of hepatocellular carcinoma (HCC) after liver resection (LR). However, these models are too complex to use in daily practice. We aimed to develop a simple model.

METHOD

We enrolled 1133 patients with newly diagnosed HCC undergoing LR. The Kaplan - Meier method and log-rank test were used for survival analysis and Cox proportional hazards analysis to identify prognostic factors associated with early recurrence (i.e., recurrence within two years after LR).

RESULTS

Early recurrence was identified in 403 (35.1%) patients. In multivariate analysis, alpha-fetoprotein (AFP) 20-399 vs. < 20 ng/ml (HR = 1.282 [95% confidence interval = 1.002-1.639]; p = 0.048); AFP ≥ 400 vs. < 20 ng/ml (HR = 1.755 [1.382-2.229]; p < 0.001); 7th edition American Joint Committee on Cancer (AJCC) stage 2 vs. 1 (HR = 1.958 [1.505-2.547]; p < 0.001); AJCC stage 3 vs. 1 (HR = 4.099 [3.043-5.520]; p < 0.001); and pathology-defined cirrhosis (HR = 1.46 [1.200-1.775]; p < 0.001) were associated with early recurrence. We constructed a predictive model with these variables, which provided three risk strata for recurrence-free survival (RFS): low risk, intermediate risk, and high risk, with two-year RFS of 79%, 57%, and 35%, respectively (p < 0.001).

CONCLUSION

We developed a simple model to predict early recurrence risk for patients undergoing LR for HCC.

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167-7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922-41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374-12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780-10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771-0.894) and 0.821 (95 % CI 0.720-0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.

Comparison of ANN and LR models for predicting Carbapenem-resistant Klebsiella pneumoniae isolates from a southern province of China's RNSS data

OBJECTIVES

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious threat to public health due to its limited treatment options and high mortality rate. This study aims to identify the risk factors of carbapenem resistance in patients with K. pneumoniae isolates and develop CRKP prediction models using logistic regression (LR) and artificial neural network (ANN) methods.

METHODS

We retrospectively analysed the data of 49,774 patients with Klebsiella pneumoniae isolates from a regional nosocomial infection surveillance system (RNSS) between 2018 and 2021. We performed logistic regression analyses to determine the independent predictors for CRKP. We then built and evaluated LR and ANN models based on these predictors using calibration curves, ROC curves, and decision curve analysis (DCA). We also applied the Synthetic Minority Over-Sampling Technique (SMOTE) to balance the data of CRKP and non-CRKP groups.

RESULTS

The LR model showed good discrimination and calibration in both training and validation sets, with areas under the ROC curve (AUROC) of 0.824 and 0.825, respectively. The DCA indicated that the LR model had clinical usefulness for decision making. The ANN model outperformed the LR model both in the training set and validation set. The SMOTE technique improved the performance of both models for CRKP detection in training set, but not in the validation set.

CONCLUSION

We developed and validated LR and ANN models for predicting CRKP based on RNSS data. Both models were feasible and reliable for CRKP inference and could potentially assist clinicians in selecting appropriate empirical antibiotics and reducing unnecessary medical resource utilization.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study

The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell's C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.

Research progresses of imaging studies on preoperative prediction of microvascular invasion of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects.

Noninvasive urinary protein signatures combined clinical information associated with microvascular invasion risk in HCC patients

BACKGROUND

Microvascular invasion (MVI) is the main factor affecting the prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to identify accurate diagnostic biomarkers from urinary protein signatures for preoperative prediction.

METHODS

We conducted label-free quantitative proteomic studies on urine samples of 91 HCC patients and 22 healthy controls. We identified candidate biomarkers capable of predicting MVI status and combined them with patient clinical information to perform a preoperative nomogram for predicting MVI status in the training cohort. Then, the nomogram was validated in the testing cohort (n = 23). Expression levels of biomarkers were further confirmed by enzyme-linked immunosorbent assay (ELISA) in an independent validation HCC cohort (n = 57).

RESULTS

Urinary proteomic features of healthy controls are mainly characterized by active metabolic processes. Cell adhesion and cell proliferation-related pathways were highly defined in the HCC group, such as extracellular matrix organization, cell-cell adhesion, and cell-cell junction organization, which confirms the malignant phenotype of HCC patients. Based on the expression levels of four proteins: CETP, HGFL, L1CAM, and LAIR2, combined with tumor diameter, serum AFP, and GGT concentrations to establish a preoperative MVI status prediction model for HCC patients. The nomogram achieved good concordance indexes of 0.809 and 0.783 in predicting MVI in the training and testing cohorts.

CONCLUSIONS

The four-protein-related nomogram in urine samples is a promising preoperative prediction model for the MVI status of HCC patients. Using the model, the risk for an individual patient to harbor MVI can be determined.

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients

BACKGROUND AND AIM

The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.

METHODS

One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

RESULTS

Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72-0.86) and 0.75 (95%CI: 0.62-0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival.

CONCLUSION

By combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making.

Scientific Hepatectomy for Hepatocellular Carcinoma

With advances in imaging technology and surgical instruments, hepatectomy can be perfectly performed with technical precision for hepatocellular carcinoma (HCC). However, the 5-year tumor recurrence rates remain greater than 70%. Thus, the strategy for hepatectomy needs to be reappraised based on insights of scientific advances. Scientific evidence has suggested that the main causes of recurrence after hepatectomy for HCC are mainly related to underlying cirrhosis and the vascular spread of tumor cells that basically cannot be eradicated by hepatectomy. Liver transplantation and systemic therapy could be the solution to prevent postoperative recurrence in this regard. Therefore, determining the severity of liver cirrhosis for choosing the appropriate surgical modality, such as liver transplantation or hepatectomy, for HCC and integrating newly emerging immune-related adjuvant and/or neoadjuvant therapy into the strategy of hepatectomy for HCC have become new aspects of exploration to optimize the strategy of hepatectomy. In this new area, hepatectomy for HCC has evolved from a pure technical concept emphasizing anatomic resection into a scientific concept embracing technical considerations and scientific advances in underlying liver cirrhosis, vascular invasion, and systemic therapy. By introducing the concept of scientific hepatectomy, the indications, timing, and surgical techniques of hepatectomy will be further scientifically optimized for individual patients, and recurrence rates will be decreased and long-term survival will be further prolonged.

Analysis of Sonazoid contrast-enhanced ultrasound for predicting the risk of microvascular invasion in hepatocellular carcinoma: a prospective multicenter study

OBJECTIVES

The aim of this study was to evaluate the potential of Sonazoid contrast-enhanced ultrasound (SNZ-CEUS) as an imaging biomarker for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

From August 2020 to March 2021, we conducted a prospective multicenter study on the clinical application of Sonazoid in liver tumor; a MVI prediction model was developed and validated by integrating clinical and imaging variables. Multivariate logistic regression analysis was used to establish the MVI prediction model; three models were developed: a clinical model, a SNZ-CEUS model, and a combined model and conduct external validation. We conducted subgroup analysis to investigate the performance of the SNZ-CEUS model in non-invasive prediction of MVI.

RESULTS

Overall, 211 patients were evaluated. All patients were split into derivation (n = 170) and external validation (n = 41) cohorts. Patients who had MVI accounted for 89 of 211 (42.2%) patients. Multivariate analysis revealed that tumor size (> 49.2 mm), pathology differentiation, arterial phase heterogeneous enhancement pattern, non-single nodular gross morphology, washout time (< 90 s), and gray value ratio (≤ 0.50) were significantly associated with MVI. Combining these factors, the area under the receiver operating characteristic (AUROC) of the combined model in the derivation and external validation cohorts was 0.859 (95% confidence interval (CI): 0.803-0.914) and 0.812 (95% CI: 0.691-0.915), respectively. In subgroup analysis, the AUROC of the SNZ-CEUS model in diameter  ≤ 30 mm and ˃ 30 mm cohorts were 0.819 (95% CI: 0.698-0.941) and 0.747 (95% CI: 0.670-0.824).

CONCLUSIONS

Our model predicted the risk of MVI in HCC patients with high accuracy preoperatively.

CLINICAL RELEVANCE STATEMENT

Sonazoid, a novel second-generation ultrasound contrast agent, can accumulate in the endothelial network and form a unique Kupffer phase in liver imaging. The preoperative non-invasive prediction model based on Sonazoid for MVI is helpful for clinicians to make individualized treatment decisions.

KEY POINTS

• This is the first prospective multicenter study to analyze the possibility of SNZ-CEUS preoperatively predicting MVI. • The model established by combining SNZ-CEUS image features and clinical features has high predictive performance in both derivation cohort and external validation cohort. • The findings can help clinicians predict MVI in HCC patients before surgery and provide a basis for optimizing surgical management and monitoring strategies for HCC patients.

Liver resection versus liver transplantation for hepatocellular carcinoma within the Milan criteria based on estimated microvascular invasion risks

Background

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) may optimize individualized treatment decision-making. This study aimed to investigate the prognostic differences between HCC patients undergoing liver resection (LR) and liver transplantation (LT) based on predicted MVI risks.

Methods

We analysed 905 patients who underwent LR, including 524 who underwent anatomical resection (AR) and 117 who underwent LT for HCC within the Milan criteria using propensity score matching. A nomogram model was used to predict preoperative MVI risk.

Results

The concordance indices of the nomogram for predicting MVI were 0.809 and 0.838 in patients undergoing LR and LT, respectively. Based on an optimal cut-off value of 200 points, the nomogram defined patients as high- or low-risk MVI groups. LT resulted in a lower 5-year recurrence rate and higher 5-year overall survival (OS) rate than LR among the high-risk patients (23.6% vs 73.2%, P < 0.001; 87.8% vs 48.1%, P < 0.001) and low-risk patients (19.0% vs 45.7%, P < 0.001; 86.5% vs 70.0%, P = 0.002). The hazard ratios (HRs) of LT vs LR for recurrence and OS were 0.18 (95% confidence interval [CI], 0.09-0.37) and 0.12 (95% CI, 0.04-0.37) among the high-risk patients and 0.37 (95% CI, 0.21-0.66) and 0.36 (95% CI, 0.17-0.78) among the low-risk patients. LT also provided a lower 5-year recurrence rate and higher 5-year OS rate than AR among the high-risk patients (24.8% vs 63.5%, P = 0.001; 86.7% vs 65.7%, P = 0.004), with HRs of LT vs AR for recurrence and OS being 0.24 (95% CI, 0.11-0.53) and 0.17 (95% CI, 0.06-0.52), respectively. The 5-year recurrence and OS rates between patients undergoing LT and AR were not significantly different in the low-risk patients (19.4% vs 28.3%, P = 0.129; 85.7% vs 77.8%, P = 0.161).

Conclusions

LT was superior to LR for patients with HCC within the Milan criteria with a predicted high or low risk of MVI. No significant differences in prognosis were found between LT and AR in patients with a low risk of MVI.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

A novel predictive model of microvascular invasion in hepatocellular carcinoma based on differential protein expression

BACKGROUND

This study aims to construct and verify a nomogram model for microvascular invasion (MVI) based on hepatocellular carcinoma (HCC) tumor characteristics and differential protein expressions, and explore the clinical application value of the prediction model.

METHODS

The clinicopathological data of 200 HCC patients were collected and randomly divided into training set and validation set according to the ratio of 7:3. The correlation between MVI occurrence and primary disease, age, gender, tumor size, tumor stage, and immunohistochemical characteristics of 13 proteins, including GPC3, CK19 and vimentin, were statistically analyzed. Univariate and multivariate analyzes identified risk factors and independent risk factors, respectively. A nomogram model that can be used to predict the presence of MVI was subsequently constructed. Then, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were conducted to assess the performance of the model.

RESULTS

Multivariate logistic regression analysis indicated that tumor size, GPC3, P53, RRM1, BRCA1, and ARG were independent risk factors for MVI. A nomogram was constructed based on the above six predictors. ROC curve, calibration, and DCA analysis demonstrated the good performance and the clinical application potential of the nomogram model.

CONCLUSIONS

The predictive model constructed based on the clinical characteristics of HCC tumors and differential protein expression patterns could be helpful to improve the accuracy of MVI diagnosis in HCC patients.

Inflammation-Related Marker NrLR Predicts Prognosis in AFP-Negative HCC Patients After Curative Resection

Background

The role of inflammation-related markers in alpha-fetoprotein (AFP) negative hepatocellular carcinoma (HCC) is not well known. This study aimed to investigate the clinical significance of inflammation-related markers in AFP-negative HCC patients after curative resection.

Methods

One thousand one hundred and seventy-nine AFP-negative HCC patients after curative resection were included. Survival rate and prognostic analysis were performed using Kaplan-Meier and Cox regression analysis. Propensity score matching (PSM) was used for patient selection.

Results

Multivariate Cox regression showed that neutrophil times γ-glutamyl transpeptidase to lymphocyte ratio (NrLR) was the independent risk factor associated with OS (p = 0.002) and RFS (p = 0.017). Low NrLR groups (n = 628) had lower rates of albumin-bilirubin (ALBI) grade 2 (p < 0.001), lower rates of bleeding and blood transfusion (p < 0.001) than high NrLR groups. Considering tumor features, low NrLR groups had lower AFP levels (p < 0.001), smaller tumor size (p < 0.001), and lower rates of Edmondson grade III-IV (p = 0.024) than high NrLR groups. After PSM, the 1-year, 3 year-, and 5-year OS rates in the low NrLR and high NrLR groups were 96.3%, 86.9%, 64.9%, and 91.4%, 76.7%, 59.5% (p < 0.001), respectively. The 1-year, 3-year, and 5-year RFS rates in the low NrLR and high NrLR groups were 80.0%, 62.9%, 47.5%, and 71.7%, 52.6%, 39.5% (p < 0.001), respectively.

Conclusion

NrLR was a poor prognostic factor for mortality and tumor recurrence in AFP-negative HCC patients after curative resection. The simple and low-cost marker could help physician to determine patients at high risk of tumor recurrence for frequent clinical surveillance.

Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

Objective

To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.

Methods

In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.

Results

1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.

Conclusion

AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.

Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence

Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Efficacy of texture analysis of pre-operative magnetic resonance imaging in predicting microvascular invasion in hepatocellular carcinoma

BACKGROUND

Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered ‘high risk’ through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication.

AIM

This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC.

METHODS

Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI (n = 15) and non-MVI (n = 35) groups based on tumour histology. Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed. Both qualitative (performed by radiologists) and quantitative data (performed by software) were obtained. Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software. Five separate methods were performed. Methods 1, 2 and 3 exclusively made use of features derived from arterial, portovenous and equilibrium phases respectively. Methods 4 and 5 made use of the comparatively significant features to attain optimal performance.

RESULTS

Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%.

CONCLUSION

Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.

Clinical application of artificial neural network (ANN) modeling to predict BRCA1/2 germline deleterious variants in Chinese bilateral primary breast cancer patients

BACKGROUND

Bilateral breast cancer (BBC), as well as ovarian cancer, are significantly associated with germline deleterious variants in BRCA1/2, while BRCA1/2 germline deleterious variants carriers can exquisitely benefit from poly (ADP-ribose) polymerase (PARP) inhibitors. However, formal genetic testing could not be carried out for all patients due to extensive use of healthcare resources, which in turn results in high medical costs. To date, existing BRCA1/2 deleterious variants prediction models have been developed in women of European or other descent who are quite genetically different from Asian population. Therefore, there is an urgent clinical need for tools to predict the frequency of BRCA1/2 deleterious variants in Asian BBC patients balancing the increased demand for and cost of cancer genetics services.

METHODS

The entire coding region of BRCA1/2 was screened for the presence of germline deleterious variants by the next generation sequencing in 123 Chinese BBC patients. Chi-square test, univariate and multivariate logistic regression were used to assess the relationship between BRCA1/2 germline deleterious variants and clinicopathological characteristics. The R software was utilized to develop artificial neural network (ANN) and nomogram modeling for BRCA1/2 germline deleterious variants prediction.

RESULTS

Among 123 BBC patients, we identified a total of 20 deleterious variants in BRCA1 (8; 6.5%) and BRCA2 (12; 9.8%). c.5485del in BRCA1 is novel frameshift deleterious variant. Deleterious variants carriers were younger at first diagnosis (P = 0.0003), with longer interval between two tumors (P = 0.015), at least one medullary carcinoma (P = 0.001), and more likely to be hormone receptor negative (P = 0.006) and HER2 negative (P = 0.001). Area under the receiver operating characteristic curve was 0.903 in ANN and 0.828 in nomogram modeling individually (P = 0.02).

CONCLUSION

This study shows the spectrum of the BRCA1/2 germline deleterious variants in Chinese BBC patients and indicates that the ANN can accurately predict BRCA deleterious variants than conventional statistical linear approach, which confirms the BRCA1/2 deleterious variants carriers at the lowest costs without adding any additional examinations.

Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database

Background

The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS).

Methods

1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared.

Results

Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division.

Conclusions

A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.

Efficacy of Postoperative Adjuvant Transcatheter Arterial Chemoembolization in Hepatocellular Carcinoma Patients With Microscopic Portal Vein Invasion

Background

Microscopic portal vein invasion (MPVI) strongly predicts poor prognosis in patients with hepatocellular carcinoma (HCC). This study aims to investigate the impact of MPVI on the efficacy of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).

Methods

From April 2014 to July 2019, a total of 512 HCC patients who underwent curative liver resection (LR) with microscopic vascular invasion (MVI) confirmed by histopathological examination were enrolled and divided into LR alone and PA-TACE groups. They were subsequently stratified into subgroups according to the presence of MPVI. Recurrence-free survival (RFS) and overall survival (OS) were compared using Kaplan–Meier curves and the log-rank test. The efficacy of PA-TACE was tested using univariate and multivariate Cox regression analyses. Sensitivity analysis was conducted after propensity score matching (PSM).

Results

Among all patients, 165 (32.3%) patients underwent PA-TACE, and 196 (38.2%) patients presented MPVI. In the entire cohort, PA-TACE and the presence of MPVI were identified as independent predictors for RFS and OS (all p<0.05). In the subgroup analysis, patients without MPVI who received PA-TACE had significantly better outcomes than those who underwent LR alone before and after PSM (all p<0.05). For patients with MPVI, PA-TACE displayed no significant benefit in terms of improving either RFS or OS, which was consistent with the results from the PSM cohort.

Conclusion

Among the HCC patients without MPVI who underwent curative liver resection, those who received PA-TACE had better RFS and OS outcomes than those who underwent LR alone. For patients with MPVI, PA-TACE had no significant effect on either RFS or OS outcomes.

A Predictive Nomogram of Early Recurrence for Patients with AFP-Negative Hepatocellular Carcinoma Underwent Curative Resection

Background: Alpha-fetoprotein-negative (<20 ng/mL) hepatocellular carcinoma (AFP-NHCC) cannot be easily diagnosed in clinical practice, which may affect early treatment and prognosis. Furthermore, there are no reliable tools for the prediction of AFP-NHCC early recurrence that have been developed currently. The objective of this study was to identify the independent risk factors for AFP-NHCC and construct an individual prediction nomogram of early recurrence of these patients who underwent curative resection. Methods: A retrospective study of 199 patients with AFP-NHCC who had undergone curative resection and another 231 patients with AFP-positive HCC were included in case-controlled analyses. All AFP-NHCC patients were randomly divided into training and validation datasets at a ratio of 7:3. The univariate and multivariate Cox proportional hazards regression analyses were applied to identify the risk factors, based on which the predictive nomogram of early recurrence was constructed in the training dataset. The area under the curve (AUC), calibration curve, and decision curve was used to evaluate the predictive performance and discriminative ability of the nomogram, and the results were validated in the validation dataset. Results: Compared to AFP-positive patients, the AFP-negative group with lower values of laboratory parameters, lower tumor aggressiveness, and less malignant magnetic resonance (MR) imaging features. AST (HR = 2.200, p = 0.009), tumor capsule (HR = 0.392, p = 0.017), rim enhancement (HR = 2.825, p = 0.002) and TTPVI (HR = 5.511, p < 0.001) were independent predictors for early recurrence of AFP-NHCC patients. The nomogram integrated these independent predictors and achieved better predictive performance with AUCs of 0.89 and 0.85 in the training and validation datasets, respectively. The calibration curve and decision curve analysis both demonstrated better predictive efficacy and discriminative ability of the nomogram. Conclusions: The nomogram based on the multivariable Cox proportional hazards regression analysis presented accurate individual prediction for early recurrence of AFP-NHCC patients after surgery. This nomogram could assist physicians in personalized treatment decision-making for patients with AFP-NHCC.

Alpha-Fetoprotein+Alkaline Phosphatase (A-A) Score Can Predict the Prognosis of Patients with Ruptured Hepatocellular Carcinoma Underwent Hepatectomy

Background

This research is aimed at establishing a scoring system alpha-fetoprotein+alkaline phosphatase (A-A score) based on preoperative serum alpha-fetoprotein (AFP) and alkaline phosphatase (ALP) levels and to investigate its clinical significance in patients with ruptured hepatocellular carcinoma (rHCC) after hepatectomy.

Methods

175 ruptured hepatocellular carcinoma (HCC) patients treated with hepatectomy were included. Survival analysis was assessed by the Kaplan-Meier method. Prognostic factors were analyzed in a multivariate model. Preoperative serum AFP and ALP values are assigned a score of 1 if they exceed the threshold value and 0 if they are below the threshold value, A-A score is obtained by summing the scores of two variables (AFP, ALP), and the predictive values of AFP, ALP, and A-A score were compared by receiver operating characteristic curve (ROC) analysis, and subgroup analyses were performed to further evaluate the power of A-A scores.

Results

Of the 175 patients, 67 (38.3%) had an A-A score of 0, 72 (41.1%) had an A-A score of 1, and 36 (20.6%) had an A-A score of 2. In multivariate analysis, the A-A score, the BCLC stage, and the extent of resection were independent predictors of OS in patients with rHCC. The 1-, 3-, and 5-year OS and RFS in patients with an A-A score of 1 were better than those with an A-A score of 0 and worse than those with an A-A score of 1 (all p < 0.05). Based on the results of ROC analysis, the A-A score is superior to AFP or ALP alone in predicting the prognosis of patients with ruptured HCC. In subgroup analysis, A-A score could accurately predict the prognosis of patients with or without microvascular invasion (MVI) and with different Child-Pugh grades or gender.

Conclusions

The A-A score can effectively predict the prognosis of patients after hepatectomy of ruptured hepatocellular carcinoma. At the same time, it also has good evaluation ability in different subgroups.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Combined Hepatocellular-Cholangiocarcinoma: What the Multidisciplinary Team Should Know

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare type of primary liver malignancy. Among the risk factors, hepatitis B and hepatitis C virus infections, cirrhosis, and male gender are widely reported. The clinical appearance of cHCC-CCA is similar to that of HCC and iCCA and it is usually silent until advanced states, causing a delay of diagnosis. Diagnosis is mainly based on histology from biopsies or surgical specimens. Correct pre-surgical diagnosis during imaging studies is very problematic and is due to the heterogeneous characteristics of the lesion in imaging, with overlapping features of HCC and CCA. The predominant histological subtype within the lesion establishes the predominant imaging findings. Therefore, in this scenario, the radiological findings characteristic of HCC show an overlap with those of CCA. Since cHCC-CCAs are prevalent in patients at high risk of HCC and there is a risk that these may mimic HCC, it is currently difficult to see a non-invasive diagnosis of HCC. Surgery is the only curative treatment of HCC-CCA. The role of liver transplantation (LT) in the treatment of cHCC-CCA remains controversial, as is the role of ablative or systemic therapies in the treatment of this tumour. These lesions still remain challenging, both in diagnosis and in the treatment phase. Therefore, a pre-treatment imaging diagnosis is essential, as well as the identification of prognostic factors that could stratify the risk of recurrence and the most adequate therapy according to patient characteristics.

Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection

Background

Early recurrence is common for hepatocellular carcinoma (HCC) after surgical resection, being the leading cause of death. Traditionally, the COX proportional hazard (CPH) models based on linearity assumption have been used to predict early recurrence, but predictive performance is limited. Machine learning models offer a novel methodology and have several advantages over CPH models. Hence, the purpose of this study was to compare random survival forests (RSF) model with CPH models in prediction of early recurrence for HCC patients after curative resection.

Methods

A total of 4,758 patients undergoing curative resection from two medical centers were included. Fifteen features including age, gender, etiology, platelet count, albumin, total bilirubin, AFP, tumor size, tumor number, microvascular invasion, macrovascular invasion, Edmondson-Steiner grade, tumor capsular, satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort. Discrimination, calibration, clinical usefulness and overall performance were assessed and compared with other models.

Results

Five hundred survival trees were used to generate the RFS model. The five highest Variable Importance (VIMP) were tumor size, macrovascular invasion, microvascular invasion, tumor number and AFP. In training, internal and external validation cohort, the C-index of RSF model were 0.725 [standard errors (SE) =0.005], 0.762 (SE =0.011) and 0.747 (SE =0.016), respectively; the Gönen & Heller's K of RSF model were 0.684 (SE =0.005), 0.711 (SE =0.008) and 0.697 (SE =0.014), respectively; the time-dependent AUC (2 years) of RSF model were 0.818 (SE =0.008), 0.823 (SE =0.014) and 0.785 (SE =0.025), respectively. The RSF model outperformed early recurrence after surgery for liver tumor (ERASL) model, Korean model, American Joint Committee on Cancer tumor-node-metastasis (AJCC TNM) stage, Barcelona Clinic Liver Cancer (BCLC) stage and Chinese stage. The RSF model is capable of stratifying patients into three different risk groups (low-risk, intermediate-risk, high-risk groups) in the training and two validation cohorts (all P<0.0001). A web-based prediction tool was built to facilitate clinical application (https://recurrenceprediction.shinyapps.io/surgery_predict/).

Conclusions

The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models. This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.

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.

Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging

OBJECTIVES

Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging.

METHODS

A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included. Based on CECT imaging, we built two gastric imaging (GI) markers, GI-marker-1 from radiomics and GI-marker-2 from DTL features, to decode LVI status. We then integrated demographics, clinical data, GI markers, radiologic interpretation, and biopsies into a Gastric Cancer Risk (GRISK) model for predicting LVI. The performance of GRISK model was tested and applied to predict survival outcomes in GC patients. Furthermore, the prognosis between LVI (+) and LVI (-) patients was compared in chemotherapy and non-chemotherapy cohorts, respectively.

RESULTS

GI-marker-1 and GI-marker-2 yield similar performance in predicting LVI in training and testing dataset. The GRISK model yields the diagnostic performance with AUC of 0.755 (95% CI 0.719-0.790) and 0.725 (95% CI 0.669-0.781) in training and testing dataset. Patients with LVI (+) trend toward lower progression-free survival (PFS) and overall survival (OS). The difference of prognosis between LVI (+) and LVI (-) was more noticeable in non-chemotherapy than that in chemotherapy group.

CONCLUSION

Radiomics and deep transfer learning features on CECT demonstrate potential power for predicting LVI in GC patients. Prospective use of a GRISK model can help to optimize individualized treatment decisions and predict survival outcomes.

Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma

Background

Prediction models for the histological grade of hepatocellular carcinoma (HCC) remain unsatisfactory. The purpose of this study is to develop preoperative models to predict histological grade of HCC based on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) radiomics. And to compare the performance between artificial neural network (ANN) and logistic regression model.

Methods

A total of 122 HCCs were randomly assigned to the training set (n=85) and the test set (n=37). There were 242 radiomic features extracted from volumetric of interest (VOI) of arterial and hepatobiliary phases images. The radiomic features and clinical parameters [gender, age, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), alanine aminotransferase (ALT), aspartate transaminase (AST)] were selected by permutation test and decision tree. ANN of arterial phase (ANN-AP), logistic regression model of arterial phase (LR-AP), ANN of hepatobiliary phase (ANN-HBP), logistic regression mode of hepatobiliary phase (LR-HBP), ANN of combined arterial and hepatobiliary phases (ANN-AP + HBP), and logistic regression model of combined arterial and hepatobiliary phases (LR-AP + HBP) were built to predict HCC histological grade. Those prediction models were assessed and compared.

Results

ANN-AP and LR-AP were composed by AST and radiomic features based on arterial phase. ANN-HBP and LR-HBP were composed by AFP and radiomic features based on hepatobiliary phase. ANN-AP + HBP and LR-AP + HBP were composed by AST and radiomic features based on arterial and hepatobiliary phases. The prediction models could distinguish between high-grade tumors [Edmondson-Steiner (E-S) grade III and IV] and low-grade tumors (E-S grade I and II) in both training set and test set. In the test set, the AUCs of ANN-AP, LR-AP, ANN-HBP, LR-HBP, ANN-AP + HBP and LR-AP + HBP were 0.889, 0.777, 0.941, 0.819, 0.944 and 0.792 respectively. The ANN-HBP was significantly superior to LR-HBP (P=0.001). And the ANN-AP + HBP was significantly superior to LR-AP + HBP (P=0.007).

Conclusions

Prediction models consisting of clinical parameters and Gd-EOB-DTPA-enhanced MRI radiomic features (based on arterial phase, hepatobiliary phase, and combined arterial and hepatobiliary phases) could distinguish between high-grade HCCs and low-grade HCCs. And the ANN was superior to logistic regression model in predicting histological grade of HCC.

Preoperative Microvascular Invasion Prediction to Assist in Surgical Plan for Single Hepatocellular Carcinoma: Better Together with Radiomics

BACKGROUND

Prediction models with or without radiomic analysis for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) have been reported, but the potential for model-predicted MVI in surgical planning is unclear. Therefore, we aimed to explore the effect of predicted MVI on early recurrence after anatomic resection (AR) and non-anatomic resection (NAR) to assist surgical strategies.

METHODS

Patients with a single HCC of 2-5 cm receiving curative resection were enrolled from 2 centers. Their data were used to develop (n = 230) and test (n = 219) two prediction models for MVI using clinical factors and preoperative computed tomography images. The two prediction models, clinico-radiologic model and clinico-radiologic-radiomic (CRR) model (clinico-radiologic variables + radiomic signature), were compared using the Delong test. Early recurrence based on model-predicted high-risk MVI was evaluated between AR (n = 118) and NAR (n = 85) via propensity score matching using patient data from another 2 centers for external validation.

RESULTS

The CRR model showed higher area under the curve values (0.835-0.864 across development, test, and external validation) but no statistically significant improvement over the clinico-radiologic model (0.796-0.828). After propensity score matching, difference in 2-year recurrence between AR and NAR was found in the CRR model predicted high-risk MVI group (P = 0.005) but not in the clinico-radiologic model predicted high-risk MVI group (P = 0.31).

CONCLUSIONS

The prediction model incorporating radiomics provided an accurate preoperative estimation of MVI, showing the potential for choosing the more appropriate surgical procedure between AR and NAR.

Performance of artificial intelligence for prognostic prediction with the albumin-bilirubin and platelet-albumin-bilirubin for cirrhotic patients with acute variceal bleeding undergoing early transjugular intrahepatic portosystemic shunt

PURPOSE

The aim of this study was to validate and compare the prognostic performance of the albumin-bilirubin (ALBI) grade, platelet-albumin-bilirubin (PALBI) grade, Child-Pugh (CP) grade, and Model for End-Stage Liver Disease (MELD) score in predicting the 1-year variceal rebleeding probability using artificial intelligence for patients with cirrhosis and variceal bleeding undergoing early transjugular intrahepatic portosystemic shunt (TIPS) procedures.

MATERIALS AND METHODS

This dual-center retrospective study included two cohorts, with patients enrolled between January 2016 and September 2018 in the training cohort and January 2017 and September 2018 in the validation cohort. In the training cohort, independent risk factors associated with the 1-year variceal rebleeding probability were identified using univariate and multivariate logistic analyses. ALBI-, PALBI-, Child-Pugh-, and MELD-based nomograms and an artificial neural network (ANN) model were established and validated internally in the training cohort and externally in the validation cohort, which included patients with variceal bleeding who were treated with preventive TIPS.

RESULTS

A total of 259 patients were included. The median follow-up periods were 24.1 and 18.9 months, and the 1-year variceal rebleeding rates were 12.3% (14/114) and 10.3% (15/145) in the training and validation cohorts, respectively. In the training cohort, all four variables were identified as independent risk factors. Four nomograms were then established and showed comparable prognostic performances after internal (C-index: 0.879, 0.829, 0.874, and 0.798) and external (C-index: 0.720, 0.719, 0.718, and 0.703) validation. The ANN demonstrated that these four variables had comparable importance in predicting the 1-year variceal rebleeding probability.

CONCLUSION

None of the four variables are optimal in predicting the 1-year variceal rebleeding probability for patients with cirrhosis and variceal bleeding undergoing early TIPS.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning

RATIONALE AND OBJECTIVES

To investigate the value of diffusion-weighted magnetic resonance imaging for the prediction of microvascular invasion (MVI) of Hepatocellular Carcinoma (HCC) using Convolutional Neural Networks (CNN).

MATERIAL AND METHODS

This study was approved by the local institutional review board and the patients' informed consent was waived. Consecutive 97 subjects with 100 HCCs from July 2012 to October 2018 with surgical resection were retrieved. All subjects with diffusion-weighted imaging (DWI) examinations were performed with single-shot echo-planar imaging in a breath-hold routine. DWI parameters were three b values of 0,100,600 sec/mm². First, apparent diffusion coefficients (ADC) images were computed by mono-exponentially fitting the three b-value points. Then, multiple 2D axial patches (28 × 28) of HCCs from b0, b100, b600, and ADC images were extracted to increase the dataset for training the CNN model. Finally, the fusion of deep features derived from three b value images and ADC was conducted based on the CNN model for MVI prediction. The data set was split into the training set (60 HCCs) and the independent test set (40 HCCs). The output probability of the deep learning model in the MVI prediction of HCCs was assessed by the independent student's t-test for data following a normal distribution and Mann-Whitney U test for data violating the normal distribution. Receiver operating characteristic curve and area under the curve (AUC) were also used to assess the performance for MVI prediction of HCCs in the fixed test set.

RESULTS

Deep features in b600 images yielded better performance (AUC = 0.74, p = 0.004) for MVI prediction than b0 (AUC = 0.69, p = 0.023) and b100 (AUC = 0.734, p = 0.011). Comparatively, deep features in the ADC map obtained lower performance (AUC = 0.71, p = 0.012) than that of the higher b value images (b600) for MVI prediction. Furthermore, the fusion of deep features from the b0, b100, b600, and ADC images yielded the best results (AUC = 0.79, p = 0.002) for MVI prediction.

CONCLUSION

Fusion of deep features derived from DWI images concerning the three b-value images and the ADC image yields better performance for MVI prediction.

Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma

Objectives

To develop and validate an MR radiomics-based nomogram to predict the presence of MVI in patients with solitary HCC and further evaluate the performance of predictors for MVI in subgroups (HCC ≤ 3 cm and > 3 cm).

Materials and Methods

Between May 2015 and October 2020, 201 patients with solitary HCC were analysed. Radiomic features were extracted from precontrast T₁WI, arterial phase, portal venous phase, delayed phase and hepatobiliary phase images in regions of the intratumoral, peritumoral and their combining areas. The mRMR and LASSO algorithms were used to select radiomic features related to MVI. Clinicoradiological factors were selected by using backward stepwise regression with AIC. A nomogram was developed by incorporating the clinicoradiological factors and radiomics signature. In addition, the radiomic features and clinicoradiological factors related to MVI were separately evaluated in the subgroups (HCC ≤ 3 cm and > 3 cm).

Results

Histopathological examinations confirmed MVI in 111 of the 201 patients (55.22%). The radiomics signature showed a favourable discriminatory ability for MVI in the training set (AUC, 0.896) and validation set (AUC, 0.788). The nomogram incorporating peritumoral enhancement, tumour growth type and radiomics signature showed good discrimination in the training (AUC, 0.932) and validation sets (AUC, 0.917) and achieved well-fitted calibration curves. Subgroup analysis showed that tumour growth type was a predictor for MVI in the HCC ≤ 3 cm cohort and peritumoral enhancement in the HCC > 3 cm cohort; radiomic features related to MVI varied between the HCC ≤ 3 cm and HCC > 3 cm cohort. The performance of the radiomics signature improved noticeably in both the HCC ≤ 3 cm (AUC, 0.953) and HCC > 3 cm cohorts (AUC, 0.993) compared to the original training set.

Conclusions

The preoperative nomogram integrating clinicoradiological risk factors and the MR radiomics signature showed favourable predictive efficiency for predicting MVI in patients with solitary HCC. The clinicoradiological factors and radiomic features related to MVI varied between subgroups (HCC ≤ 3 cm and > 3 cm). The performance of radiomics signature for MVI prediction was improved in both the subgroups.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

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.

Preoperative Estimated Risk of Microvascular Invasion is Associated with Prognostic Differences Following Liver Resection Versus Radiofrequency Ablation for Early Hepatitis B Virus-Related Hepatocellular Carcinoma

OBJECTIVES

The aim of this study was to examine prognostic differences between liver resection (LR) and percutaneous radiofrequency ablation (PRFA) for hepatocellular carcinoma (HCC) based on preoperative predicted microvascular invasion (MVI) risk.

METHODS

Data on consecutive patients who underwent LR (n = 1344) or PRFA (n = 853) for hepatitis B virus-related HCC within the Milan criteria (MC) were analyzed. A preoperative nomogram was used to estimate MVI risk. Overall survival (OS), time to recurrence, and patterns of recurrence were compared using propensity score matching.

RESULTS

The concordance indices of the nomogram to predict MVI were 0.813 and 0.781 among LR patients with HCC within the MC or ≤ 3 cm, respectively. LR and PRFA resulted in similar 5-year recurrence and OS for patients with nomogram-predicted low-risk of MVI. LR provided better 5-year recurrence and OS versus PRFA for patients with high-risk of MVI (71.6% vs. 80.7%, p = 0.013; 47.9% vs. 34.0%, p = 0.002, for HCC within the MC; 62.3% vs. 78.8%, p = 0.020; 63.6% vs. 38.3%, p = 0.015, for HCC ≤ 3 cm). Among high-risk patients, LR was associated with lower recurrence and improved OS compared with PRFA, on multivariate analysis [hazard ratio (HR) 0.78, 95% confidence interval (CI) 0.63-0.97, and HR 0.68, 95% CI 0.52-0.88, for HCC within the MC; HR 0.51, 95% CI 0.32-0.81, and HR 0.47, 95% CI 0.26-0.84, for HCC ≤ 3 cm], and resulted in less early and local recurrence than PRFA (42.4% vs. 54.8%, p = 0.007, and 31.2% vs. 46.1%, p = 0.007, for HCC within the MC; 27.9% vs. 50.8%, p = 0.016, and 15.6% vs. 39.5%, p = 0.046, for HCC ≤ 3 cm).

CONCLUSIONS

LR was oncologically superior over PRFA for early HCC patients with predicted high-risk of MVI. LR was associated with better local disease control than PRFA in these patients.

Revisiting Surgical Strategies for Hepatocellular Carcinoma With Microvascular Invasion

Although liver resection (LR) and liver transplantation (LT) are widely considered as potentially curative therapies for selected patients with hepatocellular carcinoma (HCC); however, there is still high risk of tumor recurrence in majority of HCC patients. Previous studies demonstrated that the presence of microvascular invasion (MVI), which was defined as the presence of tumor emboli within the vessels adjacent to HCC, was one of the key factors of early HCC recurrence and poor surgical outcomes after LR or LT. In this review, we evaluated the impact of current MVI status on surgical outcomes after curative therapies and aimed to explore the surgical strategies for HCC based on different MVI status with evidence from pathological examination. Surgical outcomes of HCC patients with MVI have been described as a varied range after curative therapies due to a broad spectrum of current definitions for MVI. Therefore, an international consensus on the validated definition of MVI in HCC is urgently needed to provide a more consistent evaluation and reliable prediction of surgical outcomes for HCC patients after curative treatments. We concluded that MVI should be further sub-classified into MI (microvessel invasion) and MPVI (microscopic portal vein invasion); for HCC patients with MPVI, local R0 resection with a narrow or wide surgical margin will get the same surgical results. However, for HCC patients with MI, local surgical resection with a wide and negative surgical margin will get better surgical outcomes. Nowadays, MVI status can only be reliably confirmed by histopathologic evaluation of surgical specimens, limiting its clinical application. Taken together, preoperative assessment of MVI is of utmost significance for selecting a reasonable surgical modality and greatly improving the surgical outcomes of HCC patients, especially in those with liver cirrhosis.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study

Simple Summary

Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preoperative knowledge of MVI would assist with tailored surgical strategy making to prolong patient survival. Previous radiological studies proved the role of noninvasive medical imaging in MVI prediction. However, hitherto, deep learning methods remained unexplored for this clinical task. As an end-to-end self-learning strategy, deep learning may not only achieve improved prediction accuracy, but may also visualize high-risk areas of invasion by generating attention maps. In this multicenter study, we developed deep learning models to perform MVI preoperative assessments using two imaging modalities—computed tomography (CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A head-to-head prospective validation was conducted to verify the validity of deep learning models and achieve a comparison between CT and EOB-MRI for MVI assessment. The findings put forward a better understanding of MVI preoperative prediction in HCC management.

Abstract

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities—contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038; sensitivity: 70.4% vs. 57.4%, p = 0.015; specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p < 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT.

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.

Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect

Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.

Role of Machine Learning and Artificial Intelligence in Interventional Oncology

PURPOSE OF REVIEW

The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology.

RECENT FINDINGS

With advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged. In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.

Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI

OBJECTIVES

Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore.

METHODS

This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features' associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated.

RESULTS

Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1_D%), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1_E), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1_D%, relaxation time on plain scan (T1_P), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively.

CONCLUSION

MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions.

KEY POINTS

• This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC. • The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC.

Micronvasive behaviour of single small hepatocellular carcinoma: which treatment?

BACKGROUND

Microinvasion (MI), defined as infiltration of the portal or hepatic vein or bile duct and intrahepatic metastasis are accurate indicators of a poor prognosis for mall hepatocellular carcinomas (HCC). A previous study showed that intraoperative ultrasound (IOUS) definition of MI-HCC had a high concordance with histological findings. Aim of this study is to evaluate overall survival and recurrence patterns of patients with MI-HCC submitted to hepatic resection (HR) or laparoscopic ablation therapies (LAT).

METHODS

A total of 171 consecutive patients (78 h; 93 LAT) with single, small HCC (< 3 cm) with a MI pattern at IOUS examination were compared analyzing overall survival and recurrence patterns using univariate and multivariate analysis and weighting by propensity score.

RESULTS

Overall recurrences were similar in the 2 groups (HR: 51 patients (65%); LAT: 66 patients (71%)). The rate of local tumor progression in the HR group was very low (5 pts; 6%) in comparison to LAT group (22 pts; 24%; p = 0.002). The overall survival curves of HR are significantly better than that of the LAT group (p = 0.0039). On the propensity score Cox model, overall mortality was predicted by the surgical treatment with a Hazard ratio 1.68 (1.08-2.623) (p = 0.022).

CONCLUSIONS

If technically feasible and in patients fit for surgery, HR with an adequate tumor margin should be preferred to LAT in patients with MI-HCC at IOUS evaluation, to eradicate MI features near the main nodule, which are relatively frequent even in small HCC (< 3 cm).

Microvascular Invasion Status and Its Survival Impact in Hepatocellular Carcinoma Depend on Tissue Sampling Protocol

BACKGROUND

The aim of this work is to explore the impact of the number of sampling sites (NuSS) and sampling location on microvascular invasion (MVI) detection rate and long-term survival of hepatocellular carcinoma (HCC), and determine the minimum NuSS for sufficient MVI detection.

PATIENTS AND METHODS

From January 2008 to March 2017, 1144 HCC patients who underwent hepatectomy were retrospectively enrolled. Associations between NuSS and MVI positive rates and overall survival were investigated. NuSS thresholds were determined by Chow test and confirmed prospectively in 305 patients from April 2017 to February 2019. In the prospective cohort, the distribution of MVI in different sampling locations and its prognostic effect was evaluated.

RESULTS

MVI positive rates increased as NuSS increased, steadily reaching a plateau when NuSS reached a threshold. A threshold of four, six, eight, and eight sampling sites within paracancerous parenchyma ≤ 1 cm from tumor was required for detecting MVI in solitary tumors measuring 1.0-3.0, 3.1-4.9, and ≥ 5.0 cm and multiple tumors. Patients with adequate NuSS achieved longer survival than those with inadequate NuSS [hazard ratio (HR) = 0.75, P = 0.043]. For all MVI-positive patients, MVI could be detected positive in paracancerous parenchyma ≤ 1 cm from tumor. Patients with MVI positive in paracancerous parenchyma > 1 cm had higher recurrence risk than those with MVI positive only in parenchyma ≤ 1 cm (HR = 6.05, P < 0.001).

CONCLUSIONS

Adequate NuSS is associated with higher MVI detection rate and better survival of HCC patients. We recommend four, six, eight, and eight as the cut-points for evaluating MVI sampling quality and patients' prognostic stratification in the subgroups of solitary tumors measuring 1.0-3.0 cm, 3.1-4.9 cm and ≥ 5.0 cm and multiple tumors, respectively.

Indication of Liver Transplantation for Hepatocellular Carcinoma Should Be Reconsidered in Case of Microvascular Invasion and Multilocular Tumor Occurrence

Liver transplantation (LT) is routinely performed for hepatocellular carcinoma (HCC) in cirrhosis without major vascular invasion. Although the adverse influence of microvascular invasion is recognized, its occurrence does not contraindicate LT. We retrospectively analyzed in our LT cohort the significance of microvascular invasion on survival and demonstrate bridging procedures. At our hospital, 346 patients were diagnosed with HCC, 171 patients were evaluated for LT, and 153 were listed at Eurotransplant during a period of 11 years. Among these, 112 patients received LT and were included in this study. Overall survival after 1, 3 and 5 years was 86.3%, 73.9%, and 67.9%, respectively. Microvascular invasion led to significantly reduced overall (p = 0.030) and disease-free survival (p = 0.002). Five-year disease-free survival with microvascular invasion was 10.5%. Multilocular tumor occurrence with simultaneous microvascular invasion revealed the worst prognosis. In our LT cohort, predominant bridging treatment was transarterial chemoembolization (TACE) and the number of TACE significantly correlated with poorer overall survival after LT (p = 0.028), which was confirmed in multiple Cox regression analysis for overall and disease-free survival (p = 0.015 and p = 0.011). Microvascular tumor invasion is significantly associated with reduced prognosis after LT, which is aggravated by simultaneous occurrence of multiple lesions. Therefore, indication strategies for LT should be reconsidered.

Anatomical resection is useful for the treatment of primary solitary hepatocellular carcinoma with predicted microscopic vessel invasion and/or intrahepatic metastasis

PURPOSE

The aim of this study was to evaluate anatomical resection (AR) versus non-AR for primary solitary hepatocellular carcinoma (HCC) with predicted microscopic vessel invasion (MVI) and/or microscopic intrahepatic metastasis (MIM).

METHODS

This retrospective study included 358 patients who underwent hepatectomy and had no evidence of MVI and/or MIM on preoperative imaging. The predictors of MVI and/or MIM were identified. The AR group (n = 222) and the non-AR group (n = 136) were classified by number of risk factor, and the survival rates were compared.

RESULTS

Microscopic vessel invasion and/or MIM were identified in 81 (22.6%) patients. A multivariate analysis showed that high des-gamma-carboxy prothrombin concentration [odds ratio (OR) 3.35], large tumor size (OR 3.16), and high aspartate aminotransferase concentration (OR 2.13) were significant predictors. The 5-year overall survival (OS) in the patients with zero, one, two, and three risk factors were 97.4%, 73.5%, 71.5%, and 65.5%, respectively. The OS of AR is superior to that of non-AR only in patients with one or two risk factors.

CONCLUSION

The present findings suggest that AR should be performed for patients with one or two risk factors, and that AR may prevent recurrence, as these patients are at risk of having MVI and/or MIM.