Prognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy

Background: This is the rst study to develop and evaluate a predictive model based on the microvascular invasion (MVI) classication for early recurrence and survival after curative hepatectomy in patients with early-stage hepatocellular carcinoma (HCC). Methods: The database of patients with early-stage HCC who underwent curative hepatectomy in the First Aliated Hospital of Fujian Medical University and the First Aliated Hospital of Xiamen University were retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyze disease-free survival (DFS) and overall survival (OS). Nomogram models were constructed on the datasets from the First Aliated Hospital of Fujian Medical University and the datasets were validated using bootstrap resampling with 30% samples as internal validation. Data of patients from First Aliated Hospital of Xiamen University were used for external validation. Results: A total of 703 patients with early-stage HCC were included in our study. An eight-factor nomogram for predicting recurrence or metastasis and a six-factor for predicting survival were created. The concordance indexes (C-index) were 0.775 (95% condence interval [CI], 0.720-0.830) for the DFS nomogram and 0.812 for the OS nomogram (95% CI, 0.732-0.892), respectively, in the training cohort, and 0.865 (95% CI, 0.806-0.924) and 0.839 (95% CI, 0.675-1.00), respectively, in the internal validation cohort, and 0.857 (95% CI, 0.763-0.951) and 0.842 (95% CI, 0.708-0.970), respectively, in the external validation cohort. The calibration curves showed optimal agreement between the predicted and observed DFS and OS rates. The predictive accuracy was signicantly better than that of the classic HCC staging systems. Conclusions: This study developed and validated nomograms for predicting recurrence, especially early recurrence, and overall survival in early-stage HCC patients after curative resection with high predictive accuracy. done once every 2 months for the rst 2 years after surgery. For patients who were free of cancer recurrence 2 years after surgery, a 6-month interval surveillance was performed. Disease-free survival (DFS) was dened as the duration from the rst surgery to the rst recurrence, metastasis, or death. Overall survival (OS) was dened as the duration from the rst surgery to death or the last follow-up. lipoprotein;


Introduction
Hepatocellular carcinoma (HCC) is among the most frequent causes of cancer-related deaths worldwide (1) . Despite remarkable improvements in comprehensive HCC treatment, radical surgical resection and liver transplantation are considered the only curative treatments for HCC patients classi ed as early-stage (stages 0 and A) according to the Barcelona Clinic Liver Cancer (BCLC) staging system. However, postoperative recurrence and metastasis rates of patients with early HCC vary from 50% to 70% (2) , resulting in poor overall survival (OS). Early recurrence after liver resection for HCC is the leading cause of death during the rst 2 years (3) . Therefore, developing a model for predicting postoperative recurrence, especially early recurrence, for earlystage HCC patients to guide risk strati cation and treatment is urgently needed.
Microvascular invasion (MVI), a mass of cancer cells in the vascular cavity with adhesion to endothelial cells, and only visible under a microscope (4) , has been reported by previous studies to be an indicator of early invasive manifestation of HCC. It is a crucial independent predictive factor for early recurrence and poor OS among HCC patients who underwent hepatectomy or received liver transplantation. Most BCLC early-stage HCC patients with early recurrence were pathologically veri ed as MVI positive (5)(6)(7) . Moreover, a previous study found that more invading tumor cells and multiple-invaded micro vessels might be related to poor survival and recurrence rates (4) . These ndings suggest that the BCLC staging system should reappraise HCC based on the presence or even grade of MVI to distinguish the biological behavior of early-stage HCC.
MVI is graded according to the number of cancer cells and the distance of MVI to the tumor according to the Standard for Diagnosis and Treatment of Primary Liver Cancer (8) . Although predictive models for postoperative early recurrence in HCC patients have been established, a predictive model for early BCLC stage HCC patients according to the MVI grade has not been reported.
Therefore, we retrospectively investigated the clinical and histopathological characteristics of early HCC patients after curative hepatectomy from multicenter to establish a prognostic nomogram based on MVI grade to predict early recurrence and OS.
The results of the univariate analysis for DFS and OS in the study cohort are shown in Table 2. Cox proportional hazards multivariate analysis revealed that eight factors including neutrophils, alkaline phosphatase (ALP), urea, low-density lipoprotein (LDL), apolipoprotein A1 (Apo-A1), thrombin time (TT), tumor size, and MVI grade were independent prognostic factors for DFS while six factors including TT, MVI grade, mean corpuscular hemoglobin (MCH), monocyte, prealbumin (PAB) and α-fucosidase (AFU) for OS (Table 2). Therefore, these variables were included in the subsequent analysis to establish predictive models. Establishment of nomogram model for postoperative early-relapse and evaluation of its discriminability and calibration Based on the independent prognostic factors, nomograms for DFS and OS in the study cohort were generated ( Figure 2). The results were showed in Table 3. The C-index of the nomogram for DFS was 0.775 with a 95% con dence interval (CI) of 0.720-0.830. The C-index for OS was 0.812 (95% CI, 0.732-0.892). The validation showed good consistency between the observed and  (Table   3). Taken together, the nomogram models were able to accurately predict postoperative relapse and OS in BCLC early-stage HCC patients.
Comparison of predictive accuracy between the nomogram models and the classical staging systems The predictive value of the constructed model in terms of clinical practicability was compared with that of the 8th edition American Joint Committee on Cancer (AJCC) staging system, the BCLC staging system, Japan Integrated Staging Score (JIS) and Hong Kong Liver Cancer prognostic classi cation scheme (HKLC). The results were showed in Table 3. In the training cohort,

Discussion
Although BCLC early-stage HCC patients usually have a better prognosis than those with late-stage HCC, a considerable number of patients still suffer from recurrence and metastasis. The presence of MVI is accepted worldwide as one of the most powerful predictors of poor prognosis in patients with early-stage HCC (4,8,9) . Furthermore, recent studies have found that the grade of MVI is closely related to postoperative recurrence, especially early recurrence (8, [10][11][12][13] . Of the two most used pathological staging systems for HCC, none include MVI as a criterion. A predictive model based on the classi cation of MVI for recurrence, especially early recurrence in patients with early-stage HCC, has not been reported. Therefore, we established nomograms based on the classi cation of MVI for recurrence and OS in early-stage HCC patients after curative resection, and further validation showed good agreement between the nomogram predictions and actual observations in terms of the predictive probability. In addition, our nomograms had greater predictive performance than the two classical staging systems; the BCLC and AJCC staging systems.

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The prognosis of HCC patients is mainly in uenced by the following three factors: (1) patient factors, such as immune function, nutritional status, liver function, and status of hepatitis virus infection; (2) tumor factors, such as tumor diameter, MVI classi cation, and satellite nodules; and (3) factors of treatment, such as postoperative adjuvant treatment. In our study, nine of the twelve risk factors associated with recurrence or OS were patient factors, including neutrophil, monocyte, ALP, PAB, MCH, Urea, LDL, Apo-A1, and TT, while two factors were tumor-related factors including tumor size, MVI classi cation and AFU. These results indicate that the prognosis of HCC is a multifactorial and complex process.
As the histopathological types and grades of MVI represent the histopathological changes that occur when a cancer embolus in a vessel evolves to become a satellite lesion or a metastatic site, the histopathological types of MVI can be used as a morphological marker to evaluate the biology and progression of HCC (4,14,15) . However, the detection rate of MVI is low, varying from 12.4% to 33.1% in early-stage HCC patients, and the clinical value of MVI for early-stage HCC patients after curative resection remains controversial (16)(17)(18) . In our study, the detection rate of MVI was 39.1% (275/703), and it was an independent risk factor associated with DFS and OS (Figure 1, p<0.001). It is well known that tumor size is related to patient prognosis. The presence of tumor enlargement predicts poor prognosis in patients with HCC. The corresponding cutoff value of tumor size is used in different guidelines to predict prognosis because the correlation between tumor size and poor prognosis in patients is not linear. In this study, the cutoff values were set as 5 and 10 cm. Our study identi ed tumors with a diameter > 10 cm as a signi cant risk factor for recurrence. Interestingly, although AFP is known as a common classical marker for the diagnosis and prognosis of HCC, it was not an independent factor associated with prognosis in early-stage HCC after curative hepatectomy in our study. This may be due to the low sensitivity of AFP in predicting the prognosis of early-stage HCC. It has been reported that AFP cannot be detected in 30-35% of patients with primary HCC, while an increased AFP level is also found in those with normal health (19) . Of note, AFU was a signi cantly independent factor associated with OS in early-stage HCC. It is reported that AFU is a speci c marker for HCC, which exhibits higher sensitivity and speci city than AFP in diagnosing HCC. In particular, AFU is highly and accurately discriminative of AFP-negative and early-stage HCC. Therefore, dynamic monitoring of AFU is of great signi cance for diagnosis, prognosis of early-stage HCC (20) .
Previous studies have reported that immune function and nutritional status are related to HCC patient prognosis (21)(22)(23)(24)(25) . In our nomogram models, neutrophil, monocyte, MCH, PAB and urea (the nal product of protein metabolism) are powerful immune and nutritional indices that can be used to predict prognosis. The prognosis of patients with low neutrophil and urea levels (indicating insu cient protein intake) is poor. The tumor microenvironment plays an important role in tumorigenesis.
Immune and nutritional status, being part of tumor microcirculation, will undoubtedly affect the prognosis of patients with HCC. Increasing evidence shows that basic nutritional status and systemic in ammation are related to the long-term prognosis of cancer patients (21,(26)(27)(28) . Malnutrition and low immune function not only affect the treatment effect in patients with malignant tumors, but also make HCC patients more prone to relapse and metastasis (21) .
In recent years, metabolic disorders, especially lipid metabolism disorders, have emerged as an important microenvironment for HCC pathogenesis (29,30) . LDL and Apo-A1, as indices of liver lipid metabolism, served as signi cant predictors for the prognosis of early-stage HCC in this study. It is known that changes in the metabolism of liver lipids are closely related to the occurrence of liver cancer, and in the future, non-alcoholic fatty liver disease may be identi ed as one of the main causes of primary liver cancer (31) . Moreover, previous studies have also shown that lipid metabolism disorders can promote tumor cell proliferation by inhibiting the apoptosis of liver cancer cells, resulting in a poor prognosis (32) .
Yet, there is still room for further improvement. First, our model is primarily based on retrospectively collected dataset from two Chinese institutions. Although the models performed well, the inclusion of additional cohorts from other institutions may improve the predictive accuracy of our model. Second, though the sample size in this study is adequate, a larger sample size and meaningful information including postoperative adjuvant treatment collected in the future may improve accuracy of our results.
Third, hepatitis B virus (HBV) infection, known to be associated with a poor prognosis of HCC patients, showed limited prognostic value in our study. This may have been due to some patients receiving non-standardized anti-HBV treatments, which may have affected the statistical results.
In summary, we developed and validated nomograms for predicting recurrence, especially early recurrence, and OS in early-stage HCC patients after curative resection. Their predictive performances were better than the common classical HCC staging systems, and they can help clinicians achieve better outcomes in this group of patients.