A novel prognostic nomogram based on microvascular invasion and hematological biomarkers to predict survival outcome for hepatocellular carcinoma patients

Optimizing hepatocellular carcinoma disease staging systems by incorporating tumor micronecrosis: A multi-institutional retrospective study

Tumor micronecrosis is a pathological feature that reflects malignant biological behavior in hepatocellular carcinoma (HCC). However, whether micronecrosis can optimize HCC staging systems remains unilluminated. A total of 1632 HCC patients who underwent curative hepatectomy in four institutions from January 2014 to December 2021 were enrolled in this study. Independent prognostic factors were identified, and optimized staging models were established using a training cohort (n = 934). The performance of optimized staging models was validated using an external cohort consisting of cases from three other institutions (n = 232). In addition, patients from our prospectively collected database (n = 379) tested the application effectiveness of the models. Harrel's c-statistics and the corrected Akaike information criterion (AICc) were used to assess the performance of staging models. In most of Barcelona Clinic Liver Cancer (BCLC) and tumor (T) stages, HCC patients with tumor micronecrosis showed poorer prognosis than those without. Tumor micronecrosis, microvascular invasion, multiple tumors and tumor size >2 cm were independent prognostic-related factors. The BCLC and T staging models incorporating tumor micronecrosis showed better performance than the original systems (c-statistic, 0.712 and 0.711 vs. 0.664 and 0.679; AICc, 2314.8 and 2322.3 vs. 2338.2 and 2338.1; respectively). Furthermore, the external validation cohort confirmed that the optimized staging models had improved efficiency compared with the original ones. Moreover, the prospective cohort demonstrated the applicability of the optimized staging systems. Tumor micronecrosis plays a stage-ascending role in HCC patients. The BCLC and T staging systems incorporating tumor micronecrosis can improve the prognosis stratification efficiency of patients.

A novel nomogram based on the hematological prognosis risk scoring system can predict the overall survival of patients with hepatocellular carcinoma

BACKGROUND

This study aimed to establish and validate a nomogram based on a hematological prognostic risk scoring system to predict the overall survival in patients with unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Patients diagnosed with unresectable HCC undergoing transcatheter arterial chemoembolization (TACE) in 2012-2016 and 2017-2018 were included in the development set and validation set, respectively. The clinical outcome was overall survival (OS). The LASSO regression analysis was used to construct a hematological prognostic risk scoring system (HPR) by using the 18 hematological markers of patients in the development set. Combining the features of oncology on the basis of HPR to construct a nomogram for OS. In the development set and validation sets, the C-index, calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction performance of the nomogram.

RESULTS

Multiple markers of immunity, coagulation, liver function, and nutrition, including red blood cell distribution width-coefficient of variation (RDW-CV), platelet (PLT), aspartate transferase (AST), alkaline phosphatase (ALP), prognostic nutritional index (PNI), and fibrinogen (Fib), construct the HPR. HPR was an independent risk factor for OS in patients with HCC. The C-index of the nomogram was 0.731 (95% confidence interval (CI) 0.712-0.749) and 0.696 (95% CI 0.668-0.725) in the development set and the validation set, respectively.

CONCLUSIONS

HPR was a complement to the clinical features of patients with unresectable HCC. The nomogram based on HPR proved to be a practical and effective method for prognosticating HCC patients who undergo TACE.

Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.

PURPOSE

To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.

METHODS

The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).

RESULTS

The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).

CONCLUSION

The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

Predictive value of prognostic nutritional index (PNI) in recurrent or unresectable hepatocellular carcinoma received anti-PD1 therapy

BACKGROUND

Clinical trials have shown that anti-PD1 therapy, either as a monotherapy or in combination, is effective and well-tolerated in patients with recurrent or unresectable hepatocellular carcinoma (HCC). In this study, we aimed to investigate the prognostic value of immune-nutritional biomarkers in measuring the effects of anti-PD1 therapy in these patients.

METHODS

We enrolled and followed up with 85 patients diagnosed with advanced HCC who underwent anti-PD1 therapy at the First Medical Centre of Chinese People's Liberation Army (PLA) General Hospital between January 2016 and January 2021. The retrospective analysis aimed to determine whether immune-nutritional biomarkers could serve as promising prognostic indices in these patients.

RESULTS

In this retrospective study, patients in the PNI-high group showed a better progression-free survival (PFS) compared to those in the PNI-low group (9.5 months vs. 4.2 months, P = 0.039). Similarly, the median overall survival (OS) was longer in the PNI-high group (23.9 months, 95%CI 17.45-30.35) than in the PNI-low group (11.7 months, 95%CI 9.27-14.13) (P = 0.002). These results were consistent with sub-analyses of the anti-PD1 therapy. Furthermore, both univariate and multivariate analyses indicated that a higher pre-treatment PNI ( > = 44.91) was a significant predictive factor for favorable outcomes in this patient cohort (HR = 0.411, P = 0.023).

CONCLUSION

Our study suggests that pre-treatment PNI is a critical predictive factor in patients with recurrent or unresectable HCC undergoing anti-PD1 therapy.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.

A new prognostic model predicting hepatocellular carcinoma early recurrence in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization

BACKGROUD

In this study, we aimed to develop a prognostic model to predict HCC early recurrence (within 1-year) in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).

METHODS

A total of 148 HCC patients with MVI who received PA-TACE were included in this study. The modes were verified in an internal validation cohort (n = 112) and an external cohort (n = 36). Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors relevant to early recurrence. A clinical nomogram prognostic model was established, and nomogram performance was assessed via internal validation and calibration curve statistics.

RESULTS

After data dimensionality reduction and element selection, multivariate Cox regression analysis indicated that alpha fetoprotein level, systemic inflammation response index, alanine aminotransferase, tumour diameter and portal vein tumour thrombus were independent prognostic factors of HCC early recurrence in patients with MVI who underwent PA-TACE. Nomogram with independent factors was established and achieved a better concordance index of 0.765 (95% CI: 0.691-0.839) and 0.740 (95% CI: 0.583-0.898) for predicting early recurrence in training cohort and validation cohort, respectively. Time-dependent AUC indicated comparative stability and adequate discriminative ability of the model. The DCA revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities than AJCC T stage.

CONCLUSIONS

The nomogram prognostic model showed adequate discriminative ability and high predictive accuracy.

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PURPOSE

To establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.

METHODS

In total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.

RESULTS

The risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78-0.92) and 0.85 (95% CI: 0.74-0.95), and those for the Rad-score were 0.82 (95% CI: 0.73-0.90) and 0.80 (95% CI: 0.67-0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81-0.94) in the training group and 0.89 (95% CI: 0.81-0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.

CONCLUSIONS

The nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.

Prognostic Value of Microvascular Invasion in Eight Existing Staging Systems for Hepatocellular Carcinoma: A Bi-Centeric Retrospective Cohort Study

Background

Microvascular invasion (MVI) is a significant risk factor affecting survival outcomes of patients after R0 liver resection (LR) for hepatocellular carcinoma (HCC). However, whether the existing staging systems of hepatocellular carcinoma can distinguish the prognosis of patients with MVI and the prognostic value of MVI in different subtypes of hepatocellular carcinoma remains to be clarified.

Methods

A dual-center retrospective data set of 1,198 HCC patients who underwent R0 LR was included in the study between 2014 and 2016. Baseline characteristics and staging information were collected. Homogeneity and modified Akaike information criterion (AICc) were compared between each system. And the prognostic significance of MVI for overall survival (OS) was studied in each subgroup.

Results

In the entire cohort, there were no significant survival differences between Cancer of the Liver Italian Program (CLIP) score 2 and 3 (p = 0.441), and between Taipei Integrated Scoring System (TIS) score 3 and 4 (p = 0.135). In the MVI cohort, there were no significant survival differences between Barcelona Clinic Liver Cancer stages B and C (p=0.161), CLIP scores 2 and 3 (p = 0.083), TIS scores 0 and 1 (p = 0.227), TIS scores 2 and 3 (p =0.794), Tokyo scores 3 and 4 (p=0.353), and American Joint Committee on Cancer Tumor-Node-Metastasis 7^th stage I and II (p=0.151). Among the eight commonly used HCC staging systems, the Hong Kong Liver Cancer (HKLC) staging system showed the highest homogeneity and the lowest AICc value in both the entire cohort and MVI cohort. In each subgroup of the staging systems, MVI generally exhibited poor survival outcomes.

Conclusions

The HKLC staging system was the most accurate model for discriminating the prognosis of MVI patients, among the eight staging systems. Meanwhile, our findings suggest that MVI may be needed to be incorporated into the current HCC staging systems as one of the grading criteria.

A novel classification in predicting prognosis and guiding postoperative management after R0 liver resection for patients with hepatocellular carcinoma and microvascular invasion

BACKGROUND

Microvascular invasion (MVI) is a significant risk factor affecting survival outcomes of patients after R0 liver resection (LR) for hepatocellular carcinoma (HCC). The current classification of MVI is not refined enough to prognosticate long-term survival of these patients, and a new MVI classification is needed.

METHODS

Patients with HCC who underwent R0 LR at the Eastern Hepatobiliary Surgery Hospital from January 2013 to December 2013 and with resected specimens showing MVI were included in this study with an aim to establish a novel MVI classification. The classification which was developed using multivariate cox regression analysis was externally validated.

RESULTS

There were 180 patients in the derivation cohort and 131 patients in the external validation cohort. The following factors were used for scoring: α-fetoprotein level (AFP), liver cirrhosis, tumor number, tumor diameter, MVI number, and distance between MVI and HCC. Three classes of patients could be distinguished by using the total score: class A, ≤3 points; class B, 3.5-5 points and class C, >5 points with distinct long-term survival outcomes (median recurrence free survival (mRFS), 22.6, 10.2, and 1.9 months, P < 0.001). The predictive accuracy of this classification was more accurate than the other commonly used classifications for HCC patients with MVI. In addition, the mRFS of class C patients was significantly prolonged (1.9 months vs. 6.2 months, P < 0.001) after adjuvant transcatheter arterial chemoembolization (TACE).

CONCLUSIONS

A novel MVI classification was established in predicting prognosis of HCC patients with MVI after R0 LR. Adjuvant TACE was useful for class C patients.

Albumin-Bilirubin (ALBI) and Monocyte to Lymphocyte Ratio (MLR)-Based Nomogram Model to Predict Tumor Recurrence of AFP-Negative Hepatocellular Carcinoma

Purpose

In this study, we aimed to develop a novel liver function and inflammatory markers-based nomogram to predict recurrence-free survival (RFS) for AFP-negative (<20 ng/mL) HCC patients after curative resection.

Patients and Methods

A total of 166 pathologically confirmed AFP-negative HCC patients were included at the Ningbo Medical Center Lihuili Hospital. A LASSO regression analysis was used for data dimensionality reduction and element selection. Univariate and multivariate Cox regression analyses were performed to identify the independent risk factors relevant to RFS. Finally, clinical nomogram prediction model for RFS of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Receiver operating characteristic (ROC) and decision curve analysis (DCA) curve were used to validate the performance and clinical utility of the nomogram.

Results

Multivariate Cox regression analysis indicated that ALBI grade (hazard ratio, [HR] = 2.624, 95% confidence interval [CI]: 1.391-4.949, P = 0.003), INR (HR = 2.605, 95% CI: 1.061-6.396, P = 0.037), MLR (HR = 1.769, 95% CI: 1.073-2.915, P = 0.025) and MVI (HR = 4.726, 95% CI: 2.365-9.444, P < 0.001) were independent prognostic factors of RFS. Nomogram with independent factors was established and achieved a better concordance index of 0.753 (95% CI: 0.672-0.834) for predicting RFS. The ROC found that the area under curve (AUC) was consistent with the C-index and the sensitivity was 85.4%. The risk score calculated by nomogram could divide AFP-negative HCC patients into high-, moderate- and low-risk groups (P < 0.05). DCA analysis revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities by the risk stratification than the AJCC T and BCLC stage in the prediction of AFP-negative HCC recurrence.

Conclusion

The ALBI grade- and MLR-based nomogram prognostic model for RFS showed high predictive accuracy in AFP-negative HCC patients after surgical resection.

Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma

The presence of microvascular invasion (MVI) is a critical determinant of early hepatocellular carcinoma (HCC) recurrence and prognosis. We developed a nomogram model integrating clinical laboratory examinations and radiological imaging results from our clinical database to predict microvascular invasion presence at preoperation in HCC patients. 242 patients with pathologically confirmed HCC at the Ningbo Medical Centre Lihuili Hospital from September 2015 to January 2021 were included in this study. Baseline clinical laboratory examinations and radiological imaging results were collected from our clinical database. LASSO regression analysis model was used to construct data dimensionality reduction and elements selection. Multivariate logistic regression analysis was performed to identify the independent risk factors associated with MVI and finally a nomogram for predicting MVI presence of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities. Survival analysis indicated that the probability of overall survival (OS) and recurrence-free survival (RFS) were significantly different between patients with MVI and without MVI (P < 0.05). Histopathologically identified MVI was found in 117 of 242 patients (48.3%). The preoperative factors associated with MVI were large tumor diameter (OR = 1.271, 95%CI: 1.137–1.420, P < 0.001), AFP level greater than 20 ng/mL (20–400 vs. ≤ 20, OR = 2.025, 95%CI: 1.056–3.885, P = 0.034; > 400 vs. ≤ 20, OR = 3.281, 95%CI: 1.661–6.480, P = 0.001), total bilirubin level greater than 23 umol/l (OR = 2.247, 95%CI: 1.037–4.868, P = 0.040). Incorporating tumor diameter, AFP and TB, the nomogram achieved a better concordance index of 0.725 (95%CI: 0.661–0.788) in predicting MVI presence. Nomogram analysis showed that the total factor score ranged from 0 to 160, and the corresponding risk rate ranged from 0.20 to 0.90. The DCA showed that if the threshold probability was > 5%, using the nomogram to diagnose MVI could acquire much more benefit. And the net benefit of the nomogram model was higher than single variable within 0.3–0.8 of threshold probability. In summary, the presence of MVI is an independent prognostic risk factor for RFS. The nomogram detailed here can preoperatively predict MVI presence in HCC patients. Using the nomogram model may constitute a usefully clinical tool to guide a rational and personalized subsequent therapeutic choice.

Predicting the survival rate of patients with hepatocellular carcinoma after thermal ablation by nomograms

Background

To accurately predict the survival rate of patients with hepatocellular carcinoma (HCC) undergoing thermal ablation using nomograms taking early recurrence into account as a risk factor.

Methods

A total of 591 patients receiving percutaneous thermal ablation were included in this study. The overall survival (OS) and recurrence-free survival (RFS) rate was analyzed. Two prognostic nomograms with or without taking early recurrence into account as a risk factor were constructed using the independent predictors assessed by the multivariate Cox proportional hazard model. The performance of the nomograms, in terms of discrimination and calibration, was evaluated.

Results

The cumulative RFS and OS rates at 1-, 3- and 5-year are 82.2%, 52.5%and 38.4%, 96.6%, 83.6% and 65.5%, respectively. Multivariate analysis without considering the early recurrence shows that tumor number, α-fetoprotein (AFP) level, liver function, and GGT level are associated with OS. The early recurrence, tumor number, AFP level, and liver function are considered associated with the OS when considering early recurrence. Two different nomograms were developed from the above two results. Internal validation with 1,000 bootstrapped sample sets of the two nomograms shows the concordance indexes of 0.69 (95% CI: 0.624-0.748) for the baseline nomogram and 0.81 (95% CI: 0.754-0.857) for the early recurrence-based nomogram, with the latter significantly better in discriminating performance (Z statistics =92.19, P<0.0001).

Conclusions

The survival rate of patients with HCC undergoing radical thermal ablation can be reliably predicted by the nomogram presented in this study, which was developed by taking early recurrence into account.

Nomogram Based on Systemic Immune Inflammation Index and Prognostic Nutrition Index Predicts Recurrence of Hepatocellular Carcinoma After Surgery

Background

Surgery is a potential cure for hepatocellular carcinoma (HCC), but its postoperative recurrence rate is high, its prognosis is poor, and reliable predictive indicators are lacking. This study was conducted to develop a simple, practical, and effective predictive model.

Materials and Methods

Preoperative clinical and postoperative pathological data on patients with HCC undergoing partial hepatectomies at the Third Affiliated Hospital of Soochow University from January 2010 to December 2015 were retrospectively analyzed, and a nomogram was constructed. The model performance was evaluated using C-indexes, receiver operating characteristic curves, and calibration curves. The results were verified from validation cohort data collected at the same center from January 2016 to January 2017 and compared with the traditional staging systems.

Results

Three hundred three patients were enrolled in this study: 238 in the training cohort and 65 in the validation cohort. From the univariate and multivariate Cox regression analyses in the training cohort, six independent risk factors, i.e., age, alpha-fetoprotein (AFP), tumor size, satellite nodules, systemic immune inflammation index (SII), and prognostic nutritional index (PNI), were filtered and included in the nomogram. The C-index was 0.701 [95% confidence interval (CI): 0.654–0.748] in the training cohort and 0.705 (95% CI: 0.619–0.791) in the validation cohort. The areas under the curve for the 1- and 3-year recurrence-free survival were 0.706 and 0.716 in the training cohort and 0.686 and 0.743 in the validation cohort, respectively. The calibration curves showed good agreement. Compared with traditional American Joint Committee on Cancer 8th edition (AJCC8th) and Barcelona Clinic Liver Cancer (BCLC) staging systems, our nomogram showed better predictive ability.

Conclusion

Our nomogram is simple, practical, and reliable. According to our nomogram, predicting the risk of recurrence and stratifying HCC patient management will yield the greatest survival benefit for patients.