Cloud Hosting

In this multicenter study, we designed a prediction model based on ML to accurately assessment liver fibrosis stages of CHB patients. Compared with traditional statistical models such as APRI or FIB-4, and ML model demonstrated significant improvements and was easy to process, which also suggested the great potential of ML in the field of noninvasive liver fibrosis evaluation. In addition, our study results indicated that ML model provided similar diagnostic efficacy with the reference standard liver biopsy, which may provide a reliable theoretical basis for the further development of simple, easy-to-use and accurate tools for the evaluation of liver fibrosis.

In this study, we used ML methods with the hope of more accurately assessing the staging of liver fibrosis, thereby improving the accuracy of the model. The final results revealed that our model showed superior accuracy compared to traditional serological models such as APRI or FIB-4. It is also significantly higher than the diagnostic efficacy of seventeen noninvasive liver fibrosis models in Chinese patients with hepatitis B mentioned in the study of Li et al.19. In addition, stratification analysis in inflammation subgroups was performed, and the results did show no significant impact on the performance of ML model. These findings suggest that ML model may overcome the influence of inflammation for cirrhosis evaluation, which is likely to be a potential breakthrough in non-invasive diagnosis. This was helped by a new approach to model building that had the following main advantages. First, we compared the performance of models constructed by several ML methods, and then we focused on and validated the DT model because of its better performance and ease of use. In fact, the DT model has been applied to evaluate hepatitis C liver fibrosis and has shown significant performance20. In addition, previous studies mainly used a classification method (logistic regression analysis)21, and features were selected through univariate tests (t tests, Welch tests, etc.) in many patients22,23. However, this method is often overly optimistic, prone to overfitting, and difficult to reproduce. To overcome these problems, we used integration algorithms, including mRMR and GBDT, to remove redundant features to prevent multicollinearity, and we used only high-scoring variables to construct prediction models to avoid overfitting. Second, our model allows patients to be assessed by a single blood draw without the need for additional modalities. This concept is particularly attractive for routine screening of people at high risk of disease development, such as those with advanced or severe liver fibrosis, in primary care settings. These cases which clinically suspected severe liver fibrosis previously required puncture pathology to be confirmed. However, now only need to routine serological examination to judge the probability of severe liver fibrosis, so invasive puncture examination can be avoided. Therefore, it has obvious advantages in terms of cost and prognosis. In addition, our method can be used to construct a similar model visualization to distinguish early liver fibrosis from significant liver fibrosis, and does not require specially trained clinicians, which is more convenient for clinicians in practice and of great value for clinical promotion.

In this study, we also hoped to improve the diagnostic performance of the model by identifying more specific markers and constructing the model based on the combination of known serologically relevant features. We integrated some of the most routine serological markers, in contrast to Zeng et al., who used laboratory markers such as B2-macroglobulin, haptoglobin and apolipoprotein A1, which are not commonly used in most hospitals24. Although these laboratory markers may show higher accuracy than routine serological markers, they are not suitable for practical clinical application. Our results showed of the five conventional serological markers used to construct the ML model, HBV-DNA had the greatest contribution to the model, which is consistent with the recommendation of some guidelines that patients with high HBV-DNA levels should be evaluated for noninvasive liver fibrosis4,25. HBV DNA is the marker for viral replication. For chronic HBV infection, the development of the disease is a dynamic process, and the infection status also exists for a long time. For patients with chronic HBV infection in the indeterminate phase, the results of examination alone may not be able to accurately assess the natural history stage, so dynamic follow-up observation is needed. Studies have shown that HBV DNA levels correlated with significant fibrosis in HBeAg() CHB patients. HBV DNA level could predict liver fibrosis in HBeAg() CHB patients with biopsy indication26,27.

In addition, two coagulation factors including INR and TT were integrated into the model, although the two coagulation factors are closely related in clinical practice28,29, which was may lead to over fitting of the model and overestimate the role of coagulation factors. However, we calculated the VIF value of relevant factors and did not show collinearity. Therefore, we speculate that the contribution of coagulation factors to the model should not be overestimated.

It is well known that distinguishing F0-1 from F2-4 is more challenging in many studies30,31, which is because the heterogeneity of liver fibrosis in patients with F2 liver fibrosis is more serious than that in those with F3 and 4 liver fibrosis, which generally reduces the accuracy of all classification strategies. In fact, our research results confirm that DT model has the lowest accuracy (AUC of 0.891 in training cohort and AUC of 0.876 in Validation cohort) in identifying patients with liver fibrosis grade F2. However, DT model shows high accuracy and excellent stability for each fibrosis grade in two cohorts, especially in identifying liver cirrhosis (F4), which was shows this model could be used to refine phenotypes in large research studies. Our study result also showed that the highest overall recognition rate for patients with liver cirrhosis (F4) was higher than that for patients with other stages of liver fibrosis when the model was used to classify risk prediction in the two cohorts or the whole cohort. These results suggested that our ML model may be part of a more accurate preclinical detection pathway to assess liver cirrhosis and may be used for the screening and treatment of liver cirrhosis in HBV-infected patients in routine clinical environments, although this needs to be validated in prospective studies.

This study has some limitations. First, this study was a retrospective study, which may lead to the simulation of retrospective statistics depending on too many assumptions. Future research should focus on the development of prediction and classification models based on prospective research, which will allow time evolution information to be used to evaluate, modify and reevaluate prediction models. Second, the model itself needs to be further optimized through better engineering and further development through more comprehensive integration of other clinical data to improve the overall performance of the model and achieve a more accurate noninvasive diagnosis of liver fibrosis staging. Finally, our study did not investigate the performance of ML model for classifying patients with CHB of different ethnic populations, which are also worthy of further studies in the future. Of course, in this study, we still emphasize that as conceptual research, it can still provide a certain basis for the real clinical practice in the future, although this future still needs a long way to go.

In conclusion, this study demonstrated that ML model was more accurate than traditional serological mixed biomarkers in assessing all four liver fibrosis stages in patients with CHB. In addition, the results of this study promote the goal of assessing liver fibrosis in CHB patients and improving the existing prognostic models, thereby facilitating a future prospective study design and evaluation and clinical disease surveillance and treatment. We also hope to further refine and expand this work to clarify the application of this model to a wider range of liver fibrotic diseases.

Follow this link:
A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients | Scientific ... - Nature.com