Title: Development and validation of a new predictive model for the immune tolerance stage of
chronic HBV infection based on the liver histopathological changes
Version: 3 Date: 26 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The predictive model developed for the immune tolerance (IT) stage of chronic HBV infection
was developed and validated using two hospital datasets (Taizhou and Nanjing). While this
approach is reasonable, the model’s generalizability could be questioned without external
validation on a more diverse or multi-center cohort.
-While the study mentions that there is no international consensus on the classification of HBV
infection, there is no direct comparison of the developed model with other existing models in
the field. A comparative analysis with current models or staging systems would strengthen the
manuscript’s contribution to the field and provide context on how this new model improves or
differs from previous approaches.
-The sample size from both the test group (Taizhou Hospital) and validation group (Nanjing
Drum Tower Hospital) are not mentioned in detail. The sample size could potentially affect the
statistical power and the generalizability of the findings. It would be beneficial to clarify whether
the sample size is sufficient to support the conclusions drawn, particularly for subgroups such
as patients in the IT and IC stages.
-While the predictive model is validated, the clinical utility of the model in routine practice
is not clearly discussed. How the model can be implemented in real-world clinical settings
(e.g., treatment decisions, predicting disease progression) would be a useful addition to the
manuscript. Moreover, the impact of the model on patient outcomes (e.g., survival, response to
therapy) could be explored further.
-This study is retrospective, and the lack of longitudinal data may limit the understanding of how
the progression from one stage to another occurs over time. A prospective cohort study would
be valuable in validating the findings and predicting the risk of progression to cirrhosis or HCC.
-Although liver biopsy is considered the gold standard for assessing liver fibrosis and
inflammation, it is an invasive procedure and not always representative of the overall liver
condition. Non-invasive methods such as elastography or serum markers could complement
biopsy data, and discussing this would add an extra dimension to the study.
-The manuscript touches upon differences in HBV stage definitions across various guidelines
(EASL, AASLD, etc.), but it would benefit from a more detailed discussion of how the
lack of uniform definitions might influence both clinical practice and research. This would
help contextualize the need for a new staging system and better highlight the importance of
standardization.
-The discussion mentions the presence of mild fatty changes in some of the liver biopsy samples,
which could affect the accuracy of the findings. Given that liver pathology plays a key role in
staging, the potential impact of these mild changes should be further addressed or mitigated in
future studies, perhaps by using more advanced imaging or biopsy techniques to ensure higher-
quality samples.
-The discussion notes the absence of dynamic observational data on patient prognosis. While this
is acknowledged as a limitation, this point could be expanded upon. Follow-up data are critical
in determining whether the new model can predict disease progression or response to treatment
over time. Long-term monitoring would also provide insights into the utility of the predictive
model in clinical practice, especially in relation to patient outcomes like the development of
cirrhosis or HCC.
-The discussion highlights the potential use of platelet count (PLT) in distinguishing between IT
and IC stages, citing its negative correlation with liver fibrosis. However, it would be helpful to
delve deeper into how PLT could be incorporated into clinical practice. Are there cutoff values
for PLT that could be used in routine staging? How much weight should be given to PLT in the
decision-making process compared to other markers?
-The discussion mentions the issues with liver pathology and the use of clinical indicators for
staging the ICO and IR stages. However, this could be elaborated further by comparing the
specific challenges in defining these stages based on the available data. Why are liver pathology
assessments less suitable for these stages? Could combining additional non-invasive tests (e.g.,
elastography) be considered in future studies?
Title: Development and validation of a new predictive model for the immune tolerance stage of
chronic HBV infection based on the liver histopathological changes
Version: 3 Date: 26 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The predictive model developed for the immune tolerance (IT) stage of chronic HBV infection
was developed and validated using two hospital datasets (Taizhou and Nanjing). While this
approach is reasonable, the model’s generalizability could be questioned without external
validation on a more diverse or multi-center cohort.
-While the study mentions that there is no international consensus on the classification of HBV
infection, there is no direct comparison of the developed model with other existing models in
the field. A comparative analysis with current models or staging systems would strengthen the
manuscript’s contribution to the field and provide context on how this new model improves or
differs from previous approaches.
-The sample size from both the test group (Taizhou Hospital) and validation group (Nanjing
Drum Tower Hospital) are not mentioned in detail. The sample size could potentially affect the
statistical power and the generalizability of the findings. It would be beneficial to clarify whether
the sample size is sufficient to support the conclusions drawn, particularly for subgroups such
as patients in the IT and IC stages.
-While the predictive model is validated, the clinical utility of the model in routine practice
is not clearly discussed. How the model can be implemented in real-world clinical settings
(e.g., treatment decisions, predicting disease progression) would be a useful addition to the
manuscript. Moreover, the impact of the model on patient outcomes (e.g., survival, response to
therapy) could be explored further.
-This study is retrospective, and the lack of longitudinal data may limit the understanding of how
the progression from one stage to another occurs over time. A prospective cohort study would
be valuable in validating the findings and predicting the risk of progression to cirrhosis or HCC.
-Although liver biopsy is considered the gold standard for assessing liver fibrosis and
inflammation, it is an invasive procedure and not always representative of the overall liver
condition. Non-invasive methods such as elastography or serum markers could complement
biopsy data, and discussing this would add an extra dimension to the study.
-The manuscript touches upon differences in HBV stage definitions across various guidelines
(EASL, AASLD, etc.), but it would benefit from a more detailed discussion of how the
lack of uniform definitions might influence both clinical practice and research. This would
help contextualize the need for a new staging system and better highlight the importance of
standardization.
-The discussion mentions the presence of mild fatty changes in some of the liver biopsy samples,
which could affect the accuracy of the findings. Given that liver pathology plays a key role in
staging, the potential impact of these mild changes should be further addressed or mitigated in
future studies, perhaps by using more advanced imaging or biopsy techniques to ensure higher-
quality samples.
-The discussion notes the absence of dynamic observational data on patient prognosis. While this
is acknowledged as a limitation, this point could be expanded upon. Follow-up data are critical
in determining whether the new model can predict disease progression or response to treatment
over time. Long-term monitoring would also provide insights into the utility of the predictive
model in clinical practice, especially in relation to patient outcomes like the development of
cirrhosis or HCC.
-The discussion highlights the potential use of platelet count (PLT) in distinguishing between IT
and IC stages, citing its negative correlation with liver fibrosis. However, it would be helpful to
delve deeper into how PLT could be incorporated into clinical practice. Are there cutoff values
for PLT that could be used in routine staging? How much weight should be given to PLT in the
decision-making process compared to other markers?
-The discussion mentions the issues with liver pathology and the use of clinical indicators for
staging the ICO and IR stages. However, this could be elaborated further by comparing the
specific challenges in defining these stages based on the available data. Why are liver pathology
assessments less suitable for these stages? Could combining additional non-invasive tests (e.g.,
elastography) be considered in future studies?
Please review this paper.
user
Title: Development and validation of a new predictive model for the immune tolerance stage of
chronic HBV infection based on the liver histopathological changes
Version: 3 Date: 26 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The predictive model developed for the immune tolerance (IT) stage of chronic HBV infection
was developed and validated using two hospital datasets (Taizhou and Nanjing). While this
approach is reasonable, the model’s generalizability could be questioned without external
validation on a more diverse or multi-center cohort.
-While the study mentions that there is no international consensus on the classification of HBV
infection, there is no direct comparison of the developed model with other existing models in
the field. A comparative analysis with current models or staging systems would strengthen the
manuscript’s contribution to the field and provide context on how this new model improves or
differs from previous approaches.
-The sample size from both the test group (Taizhou Hospital) and validation group (Nanjing
Drum Tower Hospital) are not mentioned in detail. The sample size could potentially affect the
statistical power and the generalizability of the findings. It would be beneficial to clarify whether
the sample size is sufficient to support the conclusions drawn, particularly for subgroups such
as patients in the IT and IC stages.
-While the predictive model is validated, the clinical utility of the model in routine practice
is not clearly discussed. How the model can be implemented in real-world clinical settings
(e.g., treatment decisions, predicting disease progression) would be a useful addition to the
manuscript. Moreover, the impact of the model on patient outcomes (e.g., survival, response to
therapy) could be explored further.
-This study is retrospective, and the lack of longitudinal data may limit the understanding of how
the progression from one stage to another occurs over time. A prospective cohort study would
be valuable in validating the findings and predicting the risk of progression to cirrhosis or HCC.
-Although liver biopsy is considered the gold standard for assessing liver fibrosis and
inflammation, it is an invasive procedure and not always representative of the overall liver
condition. Non-invasive methods such as elastography or serum markers could complement
biopsy data, and discussing this would add an extra dimension to the study.
-The manuscript touches upon differences in HBV stage definitions across various guidelines
(EASL, AASLD, etc.), but it would benefit from a more detailed discussion of how the
lack of uniform definitions might influence both clinical practice and research. This would
help contextualize the need for a new staging system and better highlight the importance of
standardization.
-The discussion mentions the presence of mild fatty changes in some of the liver biopsy samples,
which could affect the accuracy of the findings. Given that liver pathology plays a key role in
staging, the potential impact of these mild changes should be further addressed or mitigated in
future studies, perhaps by using more advanced imaging or biopsy techniques to ensure higher-
quality samples.
-The discussion notes the absence of dynamic observational data on patient prognosis. While this
is acknowledged as a limitation, this point could be expanded upon. Follow-up data are critical
in determining whether the new model can predict disease progression or response to treatment
over time. Long-term monitoring would also provide insights into the utility of the predictive
model in clinical practice, especially in relation to patient outcomes like the development of
cirrhosis or HCC.
-The discussion highlights the potential use of platelet count (PLT) in distinguishing between IT
and IC stages, citing its negative correlation with liver fibrosis. However, it would be helpful to
delve deeper into how PLT could be incorporated into clinical practice. Are there cutoff values
for PLT that could be used in routine staging? How much weight should be given to PLT in the
decision-making process compared to other markers?
-The discussion mentions the issues with liver pathology and the use of clinical indicators for
staging the ICO and IR stages. However, this could be elaborated further by comparing the
specific challenges in defining these stages based on the available data. Why are liver pathology
assessments less suitable for these stages? Could combining additional non-invasive tests (e.g.,
elastography) be considered in future studies?
assistant