Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 14 Dec 2024
Reviewer's report:
This study assesses and predicts the risk of gastric cancer based on 1,820 high-risk individuals,
using Lasso regression and Bayesian network predictive models. Overall, the structure is clear,
and the results are solid. The work can be considered for publication after revision. Below are
my comments:
1. While I understand that the authors used Lasso regression to select factors, I believe some basic
demographic factors (e.g., Gender, Age) should be forced into the Lasso regression and Bayesian
network predictive models. I leave it to the authors to decide whether additional demographic
factors should be included. The "glmnet" package may offer an option to incorporate this
constraint.
2. In the Results section, I suggest omitting the cross-validation plot that shows how the optimal
number of indicators (7) was selected. Instead, I am more interested in seeing the coefficients and
other relevant information for each factor in the Lasso regression or standard linear regression.
The current cross-validation plot can be either retained or moved to the supplementary material.
3. I could not find where Table 1 is included in the main text. Additionally, more details should be
provided to describe the demographic characteristics. It would be helpful to compare each factor
between GC patients and non-GC patients using the appropriate statistical tests and provide the
corresponding P-values in Table 1.
4. The description of the results is too brief. More details should be added, including the
motivation behind each analysis. I suggest avoiding the use of "analysis models" as subsection
titles. Instead, the titles should focus on the research question being addressed.
5. In the tables, the authors summarize many factors. However, the definitions of several
variables (e.g., smoking) are missing. I recommend adding definitions for some of the variables
in the supplementary material or within the main text.
6. For each figure, the legend should not merely contain a short title. It should include
more detailed information about how the results were generated. The figure legend should be
comprehensive enough to allow readers to interpret the figure without needing to refer to the
main text.
7. For Figures 2 and 3, please improve the resolution and make the text clearer.
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 14 Dec 2024
Reviewer's report:
This study assesses and predicts the risk of gastric cancer based on 1,820 high-risk individuals,
using Lasso regression and Bayesian network predictive models. Overall, the structure is clear,
and the results are solid. The work can be considered for publication after revision. Below are
my comments:
1. While I understand that the authors used Lasso regression to select factors, I believe some basic
demographic factors (e.g., Gender, Age) should be forced into the Lasso regression and Bayesian
network predictive models. I leave it to the authors to decide whether additional demographic
factors should be included. The "glmnet" package may offer an option to incorporate this
constraint.
2. In the Results section, I suggest omitting the cross-validation plot that shows how the optimal
number of indicators (7) was selected. Instead, I am more interested in seeing the coefficients and
other relevant information for each factor in the Lasso regression or standard linear regression.
The current cross-validation plot can be either retained or moved to the supplementary material.
3. I could not find where Table 1 is included in the main text. Additionally, more details should be
provided to describe the demographic characteristics. It would be helpful to compare each factor
between GC patients and non-GC patients using the appropriate statistical tests and provide the
corresponding P-values in Table 1.
4. The description of the results is too brief. More details should be added, including the
motivation behind each analysis. I suggest avoiding the use of "analysis models" as subsection
titles. Instead, the titles should focus on the research question being addressed.
5. In the tables, the authors summarize many factors. However, the definitions of several
variables (e.g., smoking) are missing. I recommend adding definitions for some of the variables
in the supplementary material or within the main text.
6. For each figure, the legend should not merely contain a short title. It should include
more detailed information about how the results were generated. The figure legend should be
comprehensive enough to allow readers to interpret the figure without needing to refer to the
main text.
7. For Figures 2 and 3, please improve the resolution and make the text clearer.
Please review this paper.
user
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 14 Dec 2024
Reviewer's report:
This study assesses and predicts the risk of gastric cancer based on 1,820 high-risk individuals,
using Lasso regression and Bayesian network predictive models. Overall, the structure is clear,
and the results are solid. The work can be considered for publication after revision. Below are
my comments:
1. While I understand that the authors used Lasso regression to select factors, I believe some basic
demographic factors (e.g., Gender, Age) should be forced into the Lasso regression and Bayesian
network predictive models. I leave it to the authors to decide whether additional demographic
factors should be included. The "glmnet" package may offer an option to incorporate this
constraint.
2. In the Results section, I suggest omitting the cross-validation plot that shows how the optimal
number of indicators (7) was selected. Instead, I am more interested in seeing the coefficients and
other relevant information for each factor in the Lasso regression or standard linear regression.
The current cross-validation plot can be either retained or moved to the supplementary material.
3. I could not find where Table 1 is included in the main text. Additionally, more details should be
provided to describe the demographic characteristics. It would be helpful to compare each factor
between GC patients and non-GC patients using the appropriate statistical tests and provide the
corresponding P-values in Table 1.
4. The description of the results is too brief. More details should be added, including the
motivation behind each analysis. I suggest avoiding the use of "analysis models" as subsection
titles. Instead, the titles should focus on the research question being addressed.
5. In the tables, the authors summarize many factors. However, the definitions of several
variables (e.g., smoking) are missing. I recommend adding definitions for some of the variables
in the supplementary material or within the main text.
6. For each figure, the legend should not merely contain a short title. It should include
more detailed information about how the results were generated. The figure legend should be
comprehensive enough to allow readers to interpret the figure without needing to refer to the
main text.
7. For Figures 2 and 3, please improve the resolution and make the text clearer.
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