Establishment, validation and evaluation of predictive model for early relapse after R0 resection in hepatocellular carcinoma patients with microvascular invasion

Development and validation of a nomogram for predicting the risk of vasovagal reactions after plasma donation

BACKGROUND AND OBJECTIVES

Vasovagal reactions (VVRs) are the most common adverse reactions and are frequently associated with serious donor adverse events. Even mild VVRs can lead to a significant reduction in the likelihood of subsequent donations. The purpose of this study is to explore the factors related to the occurrence of VVRs after plasma donation and to construct a nomogram to identify individuals at risk for VVRs to improve the safety of plasma donors.

MATERIALS AND METHODS

We collected the donation data from July 2019 to June 2020 from a plasma center in Sichuan, China, to explore the independent risk factors for vasovagal reactions. From these data, we constructed and validated a predictive model for vasovagal reactions.

RESULTS

VVRs after plasma donation occurred 737 times in 120 448 plasma donations (0.66%). Gender, season, donor status, weight, pulse, duration of donation, and cycle were independent risk factors for VVRs (P< 0.05). The concordance index (C-index) of a logistic model in the derivation cohort was 0.916, with a Hosmer-Lemeshow goodness-of-fit probability of 0.795. The C-index of a logistic model in the validation cohort was 0.916, with a Hosmer-Lemeshow goodness-of-fit probability of 0.224. The calibration curve showed that the predicted results were in good agreement with the actual observed results.

CONCLUSION

This study preliminarily constructed and verified a prediction model for VVRs after plasma donation. The model nomogram is practical and can identify high-risk donors.

Consensus on the tertiary prevention of primary liver cancer

To effectively prevent recurrence, improve the prognosis and increase the survival rate of primary liver cancer (PLC) patients with radical cure, the Chinese Society of Hepatology, Chinese Medical Association, invited clinical experts and methodologists to develop the Consensus on the Tertiary Prevention of Primary Liver Cancer, which was based on the clinical and scientific advances on the risk factors, histopathology, imaging finding, clinical manifestation, and prevention of recurrence of PLC. The purpose is to provide a current basis for the prevention, surveillance, early detection and diagnosis, and the effective measures of PLC recurrence.

A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm

Background

The non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.

Methods

A total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.

Results

The top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.

Conclusions

The XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.

The Dynamic Changes of AFP From Baseline to Recurrence as an Excellent Prognostic Factor of Hepatocellular Carcinoma After Locoregional Therapy: A 5-Year Prospective Cohort Study

Background

Although many studies have confirmed the prognostic value of preoperative alpha-fetoprotein (AFP) in patients with hepatocellular carcinoma (HCC), the association between AFP at baseline (b-AFP), subsequent AFP at relapse (r-AFP), and AFP alteration and overall survival in HCC patients receiving locoregional therapy has rarely been systematically elucidated.

Patients and Methods

A total of 583 subjects with newly diagnosis of virus-related HCC who were admitted to Beijing You ‘an Hospital, Capital Medical University from January 1, 2012 to December 31, 2016 were prospectively enrolled. The influence of b-AFP, subsequent r-AFP, and AFP alteration on relapse and post-recurrence survival were analyzed.

Results

By the end of follow-up, a total of 431 (73.9%) patients relapsed and 200 (34.3%) died. Patients with positive b-AFP had a 24% increased risk of recurrence compared with those who were negative. Patients with positive r-AFP had a 68% increased risk of death after relapse compared with those who were negative. The cumulative recurrence-death survival (RDS) rates for 1, 3, 5 years in patients with negative r-AFP were 85.6% (184/215), 70.2%(151/215), and 67.4%(145/215), while the corresponding rates were 75.1% (154/205), 51.2% (105/205), and 48.8% (100/205) in those with positive AFP (P<0.001). 35 (21.6%) of the 162 patients with negative b-AFP turned positive at the time of recurrence, and of this subset, only 12 (34.3%) survived. Of the 255 patients with positive b-AFP, 86 (33.7%) turned negative at the time of relapse, and of this subset, only 30 (34.9%) died. The 1-, 3-, and 5-year cumulative RDS rates were also compared among groups stratified by AFP at baseline and relapse. The present study found that patients with positive AFP at baseline and relapse, as well as those who were negative turned positive, had the shortest RDS and OS.

Conclusions

Not only AFP at baseline but also subsequent AFP at relapse can be used to predict a post-recurrence survival, which can help evaluate mortality risk stratification of patients after relapse.

A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

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The predictive model used preoperatively accessible clinical parameters and radiographic features developed and validated by us to predict micro vascular invasion (MVI), based on a large sample, two Liver Transplantation (LT) centers observed 5 years among Hepatocellular Carcinoma (HCC) patients who underwent LT.

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This is the first study to report preoperative clinical variables and radiographic features for preoperative prediction of MVI among HCC patients undergoing LT.

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Prediction of the presence of MVI can help surgical decision-making and improve surgical management for HCC to further distinguish clinical outcomes.

Purpose

The prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients.

Methods

In total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets.

Results

Univariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC.

Conclusions

The nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.