Nomograms based on inflammatory biomarkers for predicting tumor grade and micro-vascular invasion in stage I/II hepatocellular carcinoma. Biosci Rep. 2018;38. [PMID: 30254101 PMCID: PMC6239277 DOI: 10.1042/bsr20180464]

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Preoperative evaluation of MRI features and inflammatory biomarkers in predicting microvascular invasion of combined hepatocellular cholangiocarcinoma

PURPOSE

Microvascular invasion (MVI) is a significant prognostic factor in combined hepatocellular cholangiocarcinoma (cHCC-CCA). However, its diagnosis relies on postoperative histopathologic analysis. This study aims to identify preoperative inflammatory biomarkers and MR-imaging features that can predict MVI in cHCC-CCA.

METHODS

This retrospective study enrolled 119 patients with histopathologically confirmed cHCC-CCA between January 2016 and December 2021. Two radiologists, unaware of the clinical data, independently reviewed all MR image features. Univariable and multivariable analyses were performed to determine the independent predictors for MVI among inflammatory biomarkers and MRI characteristics. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic performance.

RESULTS

Multivariable logistic regression analysis identified four variables significantly associated with MVI (p < 0.05), including two inflammatory biomarkers [albumin-to-alkaline phosphatase ratio (AAPR) and aspartate aminotransferase-to-neutrophil ratio index (ANRI)] and two MRI features (non-smooth tumor margin and arterial phase peritumoral enhancement). A combined model for predicting MVI was constructed based on these four variables, with an AUC of 0.802 (95% CI 0.719-0.870). The diagnostic efficiency of the combined model was higher than that of the imaging model.

CONCLUSION

Inflammatory biomarkers and MRI features could be potential predictors for MVI in cHCC-CCA. The combined model, derived from inflammatory biomarkers and MRI features, showed good performance in preoperatively predicting MVI in cHCC-CCA patients.

Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study

Background

Currently, it is still confused whether preoperative aminotransferase-to-platelet ratio (APRI) and gamma-glutamyl transferase-to-platelet ratio (GPR) can predict microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC). We aimed to develop and validate a machine-learning integration model for predicting MVI using APRI, GPR and gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI.

Methods

A total of 314 patients from XinQiao Hospital of Army Medical University were divided chronologically into training set (n = 220) and internal validation set (n = 94), and recurrence-free survival was determined to follow up after surgery. Seventy-three patients from Chongqing University Three Gorges Hospital and Luzhou People's Hospital served as external validation set. Overall, 387 patients with solitary HCC were analyzed as whole dataset set. Least absolute shrinkage and selection operator, tenfold cross-validation and multivariate logistic regression were used to gradually filter features. Six machine-learning models and an ensemble of the all models (ENS) were built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance.

Results

APRI, GPR, HBPratio3 ([liver SI‒tumor SI]/liver SI), PLT, peritumoral enhancement, non-smooth margin and peritumoral hypointensity were independent risk factors for MVI. Six machine-learning models showed good performance for predicting MVI in training set (AUCs range, 0.793-0.875), internal validation set (0.715-0.832), external validation set (0.636-0.746) and whole dataset set (0.756-0.850). The ENS achieved the highest AUCs (0.879 vs 0.858 vs 0.839 vs 0.851) in four cohorts with excellent calibration and more net benefit. Subgroup analysis indicated that ENS obtained excellent AUCs (0.900 vs 0.809 vs 0.865 vs 0.908) in HCC >5cm, ≤5cm, ≤3cm and ≤2cm cohorts. Kaplan‒Meier survival curves indicated that ENS achieved excellent stratification for MVI status.

Conclusion

The APRI and GPR may be new potential biomarkers for predicting MVI of HCC. The ENS achieved optimal performance for predicting MVI in different sizes HCC and may aid in the individualized selection of surgical procedures.

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

A Novel Nomogram for the Preoperative Prediction of Edmondson-Steiner Grade III-IV in Hepatocellular Carcinoma Patients

Background

Edmondson-Steiner (E-S) grade is a pathological indicator of the degree of hepatocellular carcinoma (HCC) differentiation, and E-S grade III-IV is a poor prognostic factor for HCC patients. Predicting poorly differentiated HCC has essential significance for clinical decision-making. Although some studies have developed predictive models based on magnetic resonance imaging (MRI) and radiomics, radiomic features that require specific software for analysis are impractical for clinical work. This study aims to develop a novel and user-friendly nomogram model to predict E-S grade III-IV.

Patients and Methods

Medical data on patients meeting the inclusion criteria were obtained from the Nanjing Drum Tower Hospital HCC database (January 2020 to December 2022). Univariate analysis was used to screen for risk factors associated with E-S grade III-IV. A novel nomogram was established based on the subsequent multivariate logistic regression analysis. The performance of the established model was evaluated through diagnostic ability, calibration, and clinical benefits.

Results

Overall, 240 HCC patients were included in this study. Among them, 103 were highly differentiated (E-S grade I-II) HCC and 137 were poorly differentiated (E-S grade III-IV) HCC. A nomogram model that integrated alpha-fetoprotein (AFP), des-γ-carboxy prothrombin (DCP), hepatitis B virus surface antigen (HBsAg), hepatitis C virus antibodies (HCVAb), aspartate aminotransferase to lymphocyte ratio index (ALRI), and macrovascular invasion was established. The novel model had a good diagnostic performance with an area under the curve (AUC) value of 0.763. Meanwhile, the model had a diagnostic accuracy of 72.5%, a sensitivity of 78.1%, and a specificity of 65.1%. The calibration curve showed good calibration of the nomogram model (mean absolute error = 0.043), and the decision curve analysis (DCA) demonstrated that the clinical benefit was provided.

Conclusion

Our developed nomogram model could successfully predict E-S grade III-IV in HCC patients, which may be helpful in clinical decision-making.

Prognostic Analysis of Single Large Hepatocellular Carcinoma Following Radical Resection: A Single-Center Study

Objective

To investigate the survival and independent prognostic factors for single large hepatocellular carcinoma (SLHCC) after surgical resection.

Methods

Patients with SLHCC who underwent radical resection from January 2013 to December 2017 were retrospectively analyzed. The Kaplan-Meier method was used to analyze the overall survival (OS) rate and recurrence-free survival (RFS) rates. Cox forward stepwise regression was performed to analyze the independent prognostic factors.

Results

A total of 485 cases were included. The average age was 51.2±11.2 years, 88.9% had a history of hepatitis B virus infection, and most patients had normal liver function. The average tumor diameter was 8.8±3.0 cm. The 1-, 3-, and 5-year OS and RFS rates were 76.8%, 56.7%, and 45.7%, and 61.0%, 46.2%, and 34.7%, respectively. Multivariate analysis showed that liver cirrhosis (HR=1.456, P=0.004), total bilirubin (TB) ≥17.1 μmol/L (HR=1.437, P=0.011), glutamyl transferase (GGT) >60 U/L (HR=1.438, P=0.020), lactate dehydrogenase (LDH) >225 U/L (HR=1.442, P=0.007), blood loss ≥400 mL (HR=1.339, P=0.027), microvascular invasion (MVI) (HR=1.492, P=0.004), satellite lesions (HR=1.859, P<0.0001) and Edmondson-Steiner grade III+IV (HR=1.740, P=0.018) were independent risk factors for reduced OS in SLHCC patients. Sex (HR=1.763, P=0.003), liver cirrhosis (HR=1.382, P=0.007), GGT >60 U/L (HR=1.512, P=0.003), LDH >225 U/L (HR=1.480, P=0.002), MVI (HR=1.545, P=0.001), and satellite lesions (HR=1.564, P=0.001) were independent risk factors for reduced RFS. OS and RFS nomograms were constructed using risk factors with C-index values of 0.692 (95% CI: 0.659-0.724) and 0.659 (95% CI: 0.623-0.693), respectively. The Hosmer-Leme test demonstrated the good fit of both nomograms.

Conclusion

Surgical resection is the standard and effective treatment for SLHCC patients. Sex, liver cirrhosis, TB≥17.1 μmol/L, GGT>60 U/L, LDH>225 U/L, blood loss≥400 mL, MVI, Edmondson-Steiner grade III+IV, and satellite lesions were found to be independent prognostic factors in SLHCC patients following radical resection. The OS and RFS nomograms accurately predicted the prognosis of SLHCC patients.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Preoperative application of systemic inflammatory biomarkers combined with MR imaging features in predicting microvascular invasion of hepatocellular carcinoma

PURPOSE

To investigate whether systemic inflammatory biomarkers compared with the imaging features interpreted by radiologists can offer complementary value for predicting the risk of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

A total of 156 patients with histologically confirmed HCC between Jan 2018 and Dec 2020 were retrospectively enrolled in the primary cohort. Preoperative clinical-inflammatory biomarkers and MR imaging of the patients were recorded and then evaluated as an inflammatory score (Inflam-score) and imaging feature score (Radio-score). Six Inflam-scores and 12 Radio-scores were determined from each patient by univariate analysis. Logistic regression was performed to select risk factors for MVI and establish a predictive nomogram. Decision curve analysis was applied to estimate the incremental value of the Inflam-score to the Radio-score for predicting MVI.

RESULTS

Four Radio-scores and 2 Inflam-scores, namely, larger tumor size, non-smooth tumor margin, presence of satellite nodules, presence of peritumoral enhance, higher neutrophil-lymphocyte ratio (NLR), and lower prognostic nutritional index (PNI), were significantly associated with MVI (p < 0.05). An MVI risk prediction nomogram was then constructed with an area under the curve (AUC) of 0.868 (95% CI 0.806-0.931). Adding Inflam-scores to Radio-scores improved the sensitivity of the model from 60.9 to 80.4% in receiver operating characteristic (ROC) curve analysis and led to a net benefit in decision curve analysis.

CONCLUSION

Systemic inflammatory biomarkers are complementary tools that provide additional benefit to conventional imaging estimation for predicting MVI in HCC patients.

Analysis of Related Risk Factors of Microvascular Invasion in Hepatocellular Carcinoma

Objective

To forecast the onset of microvascular invasion (MVI) in patients with hepatoma by evaluating the preoperative aspartate aminotransferase-to-platelet ratio index (APRI), alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR), and other clinicopathological data.

Methods

In this study, we retrospectively analysed the clinical data of 62 patients who received radical surgery for hepa toma from 2019 to 2021. Patients were separated into the MVI-negative group and the MVI-positive group according to the postoperative pathological diagnosis. The relationships between MVI and NLR, APRI, AFP, tumor size, and other clinical data were assessed using the univariate analysis, receiver operating characteristic (ROC) curve, least absolute shrinkage and selection operator (LASSO) analysis, and logistic analysis.

Results

The ROC curve determined that the cutoff values of NLR, platelet-to-lymphocyte ratio (PLR), and APRI were 1.520, 98, and 0.275, respectively. The univariate analysis showed that the MVI-positive result was associated with five factors: tumor size (χ² = 10.620, p = 0.001), AFP (χ² = 10.524, p = 0.001), Edmondson grade (χ² = 20.736, p < 0.001), NLR (χ² = 8.744, p = 0.003), and APRI (χ² = 4.849, p = 0.028). The LASSO analysis indicated that the risk factors were the number of tumors, PLR, APRI, NLR, AFP, Edmondson grade, and tumor size. The multivariate logistic regression analysis showed that NLR ≥ 1.520 (OR 11.119, p = 0.006), APRI ≥ 0.275 (OR 12.515, p = 0.009), AFP ≥ 200 μg/mL (OR 7.823, p = 0.016), and tumor size > 3 cm (OR 7.689, p = 0.022) were independent risk factors for MVI in patients with hepatoma.

Conclusion

Preoperative NLR, APRI, AFP, and tumor size are reliable indicators for predicting the appearance of MVI in patients with hepatoma and are of great value in making detailed and reliable treatment protocols for these patients before surgery.

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.

Novel Nomogram Based on Inflammatory Markers for the Preoperative Prediction of Microvascular Invasion in Solitary Primary Hepatocellular Carcinoma

Purpose

We aimed to develop and to validate a novel nomogram based on inflammatory markers to preoperatively predict microvascular invasion (MVI) in patients with solitary primary hepatocellular carcinoma (HCC).

Patients and Methods

Data from 658 patients with solitary primary HCC who underwent hepatectomy at the First Affiliated Hospital of Zhengzhou University from June 2018 to October 2021 were retrospectively analyzed. Patients were divided into training (n=441) and validation (n=217) cohorts according to surgical data. Independent risk factors for MVI were identified via univariate and multivariate logistic regression analyses in the training cohort. A novel nomogram was developed based on the independent risk factors identified. Its accuracy was evaluated using a calibration curve and concordance index (C-index). The predictive value was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

Results

Preoperative alpha-fetoprotein >969 µg/L (P<0.001), tumor size (P=0.002), neutrophil >1.8×10⁹/L (P=0.002), gamma-glutamyl transpeptidase-to-platelet ratio (GPR) >0.32 (P=0.001), aspartate aminotransferase-to-platelet ratio (APR) >0.18 (P<0.001), gamma-glutamyl transpeptidase-to-albumin ratio (GAR) >2.30 (P=0.001), and gamma-glutamyl transpeptidase-to-lymphocyte ratio >29.58 (P<0.001) were identified as preoperative independent risk factors for MVI and were used to establish the nomogram. The C-index of the training and validation cohorts were 0.788 (95% confidence interval [CI]: 0.744–0.831) and 0.735 (95% CI: 0.668–0.802), respectively. The calibration curve analysis revealed that the standard curve fit well with the predicted curve. ROC curve analysis demonstrated high efficiency of the nomogram. DCA verified that the nomogram had notable clinical value.

Conclusion

Preoperative GPR >0.32, APR >0.18, and GAR >2.30 were independent risk factors for MVI in patients with solitary primary HCC, suggesting their utility as preoperative predictors of MVI. The novel nomogram developed and validated in this study may aid in determining optimal therapeutic approaches for patients with solitary HCC at risk for MVI.

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

BACKGROUND

Recurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels.

METHODS

Patients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model.

RESULTS

A total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory.

CONCLUSIONS

The predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.

Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics

Objectives

Methods

Results

Conclusion

Effect of Microvascular Invasion on the Prognosis in Hepatocellular Carcinoma and Analysis of Related Risk Factors: A Two-Center Study

Objective: Microvascular invasion is considered to initiate intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). We aimed to analyze the effect of MVI on the prognosis in HCC and identify related risk factors for microvascular invasion (MVI).

Methods: The clinical data of 553 HCC patients who underwent liver surgery at Qingdao University from January 2014 to December 2018 and 89 patients at Beijing Tsinghua Changgung Hospital treated between October 2014 and October 2019 were collected retrospectively. We explored the impact of MVI on the prognosis of patients with HCC using Kaplan-Meier analysis. We conducted logistic regression analysis to identify variables significantly related to MVI.

Results: Pathological examination confirmed the presence of MVI in 265 patients (41.3%). Six factors independently correlated with MVI were incorporated into the multivariate logistic regression analysis: Edmondson-Steiner grade [odds ratio (OR) = 3.244, 95%CI: 2.243–4.692; p < 0.001], liver capsule invasion (OR = 1.755; 95%CI: 1.215–2.535; p = 0.003), bile duct tumor thrombi (OR = 20.926; 95%CI: 2.552–171.553; p = 0.005), α-fetoprotein (> 400 vs. < 400 ng/ml; OR = 1.530; 95%CI: 1.017–2.303; p = 0.041), tumor size (OR = 1.095; 95%CI: 1.027–1.166; p = 0.005), and neutrophil-lymphocyte ratio (OR = 1.086; 95%CI: 1.016–1.162; p = 0.015). The area under the receiver operating characteristic curve (AUC) was 0.743 (95%CI: 0.704–0.781; p < 0.001), indicating that our logistic regression model had significant clinical usefulness.

Conclusions: We analyzed the effect of MVI on the prognosis in HCC and evaluated the risk factors for MVI, which could be helpful in making decisions regarding patients with a high risk of recurrence.

Development and Evaluation of Nomograms to Predict the Cancer-Specific Mortality and Overall Mortality of Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type among primary liver cancers (PLC). With its poor prognosis and survival rate, it is necessary for HCC patients to have a long-term follow-up. We believe that there are currently no relevant reports or literature about nomograms for predicting the cancer-specific mortality of HCC patients. Therefore, the primary goal of this study was to develop and evaluate nomograms to predict cancer-specific mortality and overall mortality. Data of 45,158 cases of HCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) program database between 2004 and 2013, which were then utilized to develop the nomograms. Finally, the performance of the nomograms was evaluated by the concordance index (C-index) and the area under the time-dependent receiver operating characteristic (ROC) curve (td-AUC). The categories selected to develop a nomogram for predicting cancer-specific mortality included marriage, insurance, radiotherapy, surgery, distant metastasis, lymphatic metastasis, tumor size, grade, sex, and the American Joint Committee on Cancer (AJCC) stage; while the marriage, radiotherapy, surgery, AJCC stage, grade, race, sex, and age were selected to develop a nomogram for predicting overall mortality. The C-indices for predicted 1-, 3-, and 5-year cancer-specific mortality were 0.792, 0.776, and 0.774; the AUC values for 1-, 3-, and 5-year cancer-specific mortality were 0.830, 0.830, and 0.830. The C-indices for predicted 1-, 3-, and 5-year overall mortality were 0.770, 0.755, and 0.752; AUC values for predicted 1-, 3-, and 5-year overall mortality were 0.820, 0.820, and 0.830. The results showed that the nomograms possessed good agreement compared with the observed outcomes. It could provide clinicians with a personalized predicted risk of death information to evaluate the potential changes of the disease-specific condition so that clinicians can adjust therapy options when combined with the actual condition of the patient, which is beneficial to patients.

A nomogram based on combining systemic and hepatic inflammation markers for predicting microscopic bile duct tumour thrombus in hepatocellular carcinoma

Background

Bile duct invasion is a relatively rare event and is not well characterised in hepatocellular carcinoma (HCC). It remains very difficult to diagnose HCC with bile duct tumour thrombus (BDTT) before surgery. Increasing evidence has revealed that inflammation plays a critical role in tumorigenesis. This study aimed to develop nomograms based on systemic and hepatic inflammation markers to predict microscopic BDTT (micro-BDTT) before surgery in HCC.

Methods

A total of 723 HCC patients who underwent hepatectomy as initial therapy between January 2012 and June 2020 were included in the study. Logistic regression analysis was used to identify independent risk factors for micro-BDTT. The nomograms were constructed using significant predictors, including α-fetoprotein (AFP), alkaline phosphatase (ALP), direct bilirubin (DB), prognostic nutritional index (PNI), and γ-glutamyl transferase (γ-GT)/alanine aminotransferase (ALT). The prediction accuracies of the nomograms were evaluated using the area under the receiver operating characteristic (ROC) curve.

Results

AFP, ALP, DB, PNI, and γ-GT/ALT were independent risk factors for predicting micro-BDTT (P = 0.036, P = 0.004, P = 0.013, P = 0.012, and P = 0.006, respectively), which were assembled into the nomograms. The area under the ROC curve of the nomograms combining PNI and γ-GT/ALT for predicting micro-BDTT was 0.804 (95% confidence interval [CI]: 0.730–0.878). The sensitivity and specificity values when used in predicting micro-BDTT before surgery were 0.739 (95% CI: 0.612–0.866) and 0.781 (95% CI: 0.750–0.813), respectively.

Conclusions

The nomogram based on combining systemic and hepatic inflammation markers is suitable for predicting micro-BDTT before surgery in HCC patients, leading to a rational therapeutic choice for HCC.

The C-reactive protein to albumin ratio is an excellent prognostic predictor for gallbladder cancer

A number of inflammation indicators based on C-reactive protein (CRP) and albumin have been widely used to predict the prognosis in several types of tumors, but their functions in gallbladder cancer (GBC) have rarely been explored. The aim of our study is to evaluate and compare the prognostic values of the C-reactive protein to albumin ratio (CAR), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS) and high-sensitivity modified Glasgow prognostic score (HS-mGPS) in patients with GBC. 144 GBC patients who received curative surgery in our hospital from January 2010 to May 2017 were enrolled in this research. The Kaplan-Meier analysis showed that the median OS of the patients in the high CAR group was significantly shorter than the patients in the low group (p < 0.001), and higher scores of GPS, mGPS and HS-mGPS were also associated with decreased OS, respectively. However, according to the Receiver Operating Characteristic (ROC) curve, the CAR was superior to the other prognostic scores in determining the prognosis for the GBC patients. In the multivariate analysis, CAR was verified as an independent risk factor for poor prognosis, together with tumor differentiation, T stage and postoperative complications. All in all, compared to the other three CRP-albumin-related prognostic predictors, CRA is a better indicator in predicting poor long-term outcomes in GBC patients after radical surgery.

Platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios predict tumor size and survival in HCC patients: Retrospective study

Background

Inflammation is a recognized concomitant of hepatocellular carcinoma (HCC) and its indices are prognostically useful.

Aims

To evaluate two commonly used inflammatory indices, neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), to examine their relationship to maximum tumor diameter (MTD) and to survival.

Methods

A database of 1024 prospectively-accrued HCC patients was examined, who had full baseline tumor parameter data, including CT scan information on HCC size and whose survival was known. Analyses of NLR and PLR were correlated to MDT and to survival. NLR and PLR cutoffs were calculated from receiver operator characteristic curves.

Results

Every MTD pair had significantly different PLR values, for MTD groups of groups <2/≥2, <3/≥3, <4/≥4, <5/≥5 cm. However there were few significant differences in NLR values. Logistic regression models of different MTD groups likewise showed significance for PLR. Patients with both low NLR and low PLR had the longest overall survival compared to all the other 3 combinations of NLR and PLR. In a Cox regression analysis, univariate models on NLR (≤3.02/>3.02) and PLR (≤6.82/>6.82) groups, showed significance for PLR, p = 0.034 and approaching significance for NLR, p = 0.057.

Conclusions

MTD pairs down to <2/≥2 cm showed significance for PLR, survival showed significance for PLR and almost for NLR.

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In HCC studies, there is a need for tumor markers, especially in that 50% of patients who are AFP negative. Most HCCs present at a size that is too large for curative therapies.

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We evaluated the commonly used NLR and PLR in relation to the smallest detectable HCC tumor size and to survival.

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We found that PLR is elevated down to resec cm diameter tumors and both NLR and PLR relate to survival.

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PLR thus has potential for widespread use in HCC prognostication and in screening patients with hepatitis or cirrhosis to identify small and thus potentially curable tumors.

Development and validation of a nomogram to predict the prognosis of patients with squamous cell carcinoma of the bladder

Background: The present study aimed to develop and validate a nomogram based on expanded TNM staging to predict the prognosis for patients with squamous cell carcinoma of the bladder (SCCB).

Methods: A total of 595 eligible patients with SCCB identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training set (n = 416) and validation set (n = 179). The likelihood ratio test was used to select potentially relevant factors for developing the nomogram. The performance of the nomogram was validated on the training and validation sets using a C-index with 95% confidence interval (95% CI) and calibration curve, and was further compared with TNM staging system.

Results: The nomogram included six factors: age, T stage, N stage, M stage, the method of surgery and tumor size. The C-indexes of the nomogram were 0.768 (0.741–0.795) and 0.717 (0.671–0.763) in the training and validation sets, respectively, which were higher than the TNM staging system with C-indexes of 0.580 (0.543–0.617) and 0.540 (0.484–0.596) in the training and validation sets, respectively. Furthermore, the decision curve analysis (DCA) proved that the nomogram provided superior clinical effectiveness.

Conclusions: We developed a nomogram that help predict individualized prognosis for patients with SCCB.

Dynamic Changes in the Neutrophil-to-Lymphocyte Ratio Predict the Prognosis of Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization

Purpose

To examine the effect of dynamic changes in neutrophil-to-lymphocyte ratio (NLR) on tumor response and overall survival (OS) in patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).

Patients and Methods

Data from 181 patients with HCC were retrospectively collected. White blood cell, neutrophil and lymphocyte counts, and the NLR were obtained 1–3 days before as well as 3–6 weeks and 3 months after TACE. Patients were divided into two groups at each time point according to the mean value of NLR, and also divided into continuous decrease, fluctuating increase-decrease (I-D), fluctuating decrease-increase (D-I), and continuous increase groups according to the dynamic changes in the NLR. The dynamic changes in blood counts and NLR were analyzed using repeated-measures ANOVA. The odds ratios (ORs) for tumor response in different NLR groups were examined using a multivariate logistic regression model. Finally, the prognostic value of the dynamic changes in the NLR was examined using Cox regression models.

Results

Continuous decline of white blood cell counts, neutrophil counts and lymphocyte counts were observed at 3–6 weeks and 3 months after TACE treatment. The NLR increased slightly and then decreased substantially in responders, while it increased slightly and then significantly in non-responders, with a significant interaction effect of Time × Tumor response (P = 0.005). NLR grouping before TACE, 3–6 weeks and 3 months after TACE was not associated with tumor response, and only 3 months after TACE did, it shows a significant difference in univariate survival analyses (NLR > 2.5 vs NLR ≤ 2.5, hazard ratio [HR] = 2.442, 95% confidence interval (CI): 1.545, 3.860). The changes in the NLR were significantly correlated with tumor response and OS. Non-responders for TACE were more common in the continuous NLR increase group (OR = 6.230, 95% CI: 1.848–21.001) and in the fluctuating D-I group (OR = 5.702, 95% CI: 1.480–21.957). Multivariate analyses revealed that these two patient groups also showed poorer OS (HR = 2.351, 95% CI: 1.120–4.605 and HR = 2.320, 95% CI: 1.187–4.533, respectively).

Conclusion

Dynamic changes in the NLR may be better predictors of tumor response and OS than static NLR values, but more data are needed.

Serum metabolites may be useful markers to assess vascular invasion and identify normal alpha-fetoprotein in hepatocellular carcinoma undergoing liver resection: a pilot study

Purpose

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a dismal prognosis. Vascular invasion, among others, is the most robust indicator of postoperative recurrence and overall survival after liver resection for HCC. Few studies to date have attempted to search for effective markers to predict vascular invasion before the operation. The current study would examine the plasma metabolic profiling via ¹H-NMR of HCC patients undergoing liver resection and aim to search for potential biomarkers in the early detection of HCC with normal alpha-fetoprotein (AFP) and the diagnosis of vascular invasion preoperatively.

Materials and methods

HCC patients scheduled to receive liver resections for their HCC were recruited and divided into two separate groups, investigation cohort and validation cohort. Their preoperative blood samples were collected and subjected to a comprehensive metabolomic profiling using ¹H-nuclear magnetic resonance spectroscopy (NMR).

Results

There were 35 HCC patients in the investigation group and 22 patients in the validation group. Chronic hepatitis B remained the most common etiology of HCC, followed by chronic HCV infection. The two study cohorts were essentially comparable in terms of major clinicopathological variables. After ¹H-nuclear NMR analysis, we found in the investigation cohort that HCC with normal alpha-fetoprotein (AFP < 15 ng/mL) had significantly higher serum level of O-acetylcarnitine than those with higher AFP (AFP ≥ 15 ng/mL, P = 0.025). In addition, HCC with microscopic vascular invasion (VI) had significantly higher preoperative serum level of formate than HCC without microscopic VI (P = 0.023). These findings were similar in the validation cohort.

Conclusion

A comprehensive metabolomic profiling of HCC demonstrated that serum metabolites may be utilized to assist the early diagnosis of AFP-negative HCC patients and recognition of microvascular invasion in order to facilitate preoperative surgical planning and postoperative follow-up. Further, larger scale prospective studies are warranted to consolidate our findings.

Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an important prognostic factor affecting early recurrence and overall survival in hepatocellular carcinoma (HCC) patients after hepatectomy and liver transplantation, but it can be determined only in surgical specimens. Accurate preoperative prediction of MVI is conducive to clinical decisions.

AIM

To develop and validate a preoperative prediction model for MVI in patients with HCC.

METHODS

Data from 454 patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Nanjing Medical University between May 2016 and October 2019 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Logistic regression analysis was used to identify variables significantly associated with MVI that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by using R software.

RESULTS

MVI was confirmed in 209 (46.0%) patients by a pathological examination. Multivariate logistic regression analysis identified four risk factors independently associated with MVI: Tumor size [odds ratio (OR) = 1.195; 95% confidence interval (CI): 1.107–1.290; P < 0.001], number of tumors (OR = 4.441; 95%CI: 2.112–9.341; P < 0.001), neutrophils (OR = 1.714; 95%CI: 1.036–2.836; P = 0.036), and serum α-fetoprotein (20–400 ng/mL, OR = 1.955; 95%CI: 1.055–3.624; P = 0.033; >400 ng/mL, OR = 3.476; 95%CI: 1.950–6.195; P < 0.001). The concordance index was 0.79 (95%CI: 0.74–0.84) and 0.81 (95%CI: 0.74–0.89) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes.

CONCLUSION

We have developed and validated a preoperative prediction model for MVI in patients with HCC. The model could aid physicians in clinical treatment decision making.

Reply to: Comments on "Nomograms based on inflammatory biomarkers for predicting tumor grade and micro-vascular invasion in stage I/II hepatocellular carcinoma"

We appreciate to receive commentary from Dr Guangtong Deng and Dr Liang Xiao to our article, "Nomograms based on inflammatory biomarkers for predicting tumor grade and micro-vascular invasion in stage I/II hepatocellular carcinoma". First, neutrophil-to-lymphocyte ratio (NLR) and derived NLR (dNLR) are two different parameters. Some studies show that NLR is inconsistent with dNRL in prognostic value through multivariate Cox regression, therefore, it is reasonable that both NLR and dNLR entered into multivariate analysis simultaneously. Second, it is common that articles of predictive nomograms turned continuous variables into categorical variables. The reason is that the categorization of patient clinical variables is beneficial to doctors to make decisions based on the risk level of individual patients in clinical. At last, multicenter validation is quite difficult and we have listed the shortcomings in the limitations of our article. Further validation will need the joint efforts by other institutions.

Commentary on: Nomograms based on inflammatory biomarkers for predicting tumor grade and microvascular invasion in stage I/II hepatocellular carcinoma

Some doubts were generated during the reading of nomograms based on inflammatory biomarkers for preoperatively predicting tumor grade and microvascular invasion in stage I/II hepatocellular carcinoma (HCC). We would like to highlight and discuss with authors. First, neutrophil-lymphocyte ratio (NLR) and derived NLR (dNLR) should not be entered into multivariate analysis simultaneously. Second, authors should clarify how the cutoffs of these variables including lymphocyte-monocyte ratio (LMR), dNLR, age and tumor size were set. We insist that the type of variables should be consistent when we carry out the analysis and establish the nomogram. Last, we have to point out that Li et al.’s (Biosci. Rep. (2018), 38) study failed to validate nomograms using an independent dataset.

Preoperative neutrophil-lymphocyte ratio predicts the risk of microvascular invasion in hepatocellular carcinoma: A meta-analysis

BACKGROUND

Preoperative estimation of microvascular invasion is of great significance for the clinical decision making in hepatocellular carcinoma. Neutrophil-lymphocyte ratio (NLR) has been reported to be correlated with the poor prognosis of hepatocellular carcinoma. However, the conclusions are conflicting on whether high preoperative NLR level is associated with the presence of microvascular invasion.

AIM

To evaluate the association between preoperative NLR level and the risk of microvascular invasion in hepatocellular carcinoma.

METHODS

Relevant studies were identified by searching PubMed and Embase through February 2019. Fixed or random models were applied to analyze the data based on the heterogeneity. Subgroup, sensitivity, and publication bias analyses were performed. Review Manager 5.3 and STATA software were used for the meta-analysis.

RESULTS

A total of 15 studies were eventually included in this meta-analysis. Pooled data based on retrospective cohort studies showed there are more hepatocellular carcinoma patients with vascular invasion (OR 1.74; 95% Cl 1.42, 2.12; P < 0.001) and microvascular invasion (OR 1.62 95% Cl 1.39, 1.89; P < 0.001) in the high NLR group than in the low NLR group. Of case-control studies, a higher preoperative NLR level was found in the microvascular invasion positive group than in the microvascular invasion negative group (OR 0.62; 95% Cl 0.35, 0.90; P < 0.001). The subgroup, sensitivity, and publication bias analyses did not change the results.

CONCLUSION

A higher preoperative NLR level is positively correlated with the risk of microvascular invasion in hepatocellular carcinoma.

The Role of AMP-Activated Protein Kinase as a Potential Target of Treatment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the fifth most frequent cancer worldwide with a very high recurrence rate and very dismal prognosis. Diagnosis and treatment in HCC remain difficult, and the identification of new therapeutic targets is necessary for a better outcome of HCC treatment. AMP-Activated Protein Kinase (AMPK) is an essential intracellular energy sensor that plays multiple roles in cellular physiology and the pathological development of chronic diseases. Recent studies have highlighted the important regulation of AMPK in HCC. This review aims to comprehensively and critically summarize the role of AMPK in HCC. Methods: Original studies were retrieved from NCBI database with keywords including AMPK and HCC, which were analyzed with extensive reading. Results: Dysregulation of the kinase activity and expression of AMPK was observed in HCC, which was correlated with survival of the patients. Loss of AMPK in HCC cells may proceed cell cycle progression, proliferation, survival, migration, and invasion through different oncogenic molecules and pathways. Conclusions: We identified several AMPK activators which may possess potential anti-HCC function, and discussed the clinical perspective on the use of AMPK activators for HCC therapy.