Title: Predicting the risk of lymph node metastasis in colon cancer: development and validation
of an online dynamic nomogram based on multiple preoperative data
Version: 2 Date: 14 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the introduction discusses various factors (e.g., symptoms, tumor markers), it would
benefit from specifying some of the key clinical or laboratory factors that are anticipated to be
significant in the prediction of LNM, offering a clearer view of the model's predictors.
The introduction generalizes some points about CRC research but does not provide specific
examples of studies that might have successfully addressed LNM prediction or those that fell
short, which could help contextualize the gap more specifically.
-Although excluding patients with missing data or high missing rates is reasonable, this approach
might lead to selection bias. It would be beneficial to explore if a sensitivity analysis or
alternative methods like multiple imputation could be used to mitigate potential biases arising
from missing data.
-While the use of LASSO helps mitigate overfitting, the large number of variables being
considered might still introduce complexity. Some justification of why all 21 variables are
necessary (and how each contributes to model performance) would enhance transparency.
The study includes endoscopic data as a predictor, but there is limited information on how
endoscopic findings are specifically incorporated into the model. Clarifying this could help
readers better understand the role of this factor.
-You clearly highlight the clinical importance of preoperative prediction for LNM, especially
in colon cancer (CC). Perhaps, a brief comparison with other existing tools/models for LNM
prediction would further emphasize the novelty and advantage of your model. For example,
you could mention if the nomogram's performance is significantly better than commonly used
clinical tools, such as the TNM staging system, in terms of accuracy, usability, or clinical utility.
-You speculated that tumor ulcers are correlated with increased LNM in CC patients, but also
mentioned that the exact mechanism remains unclear. Expanding this hypothesis could make
the discussion even more insightful. You could mention the potential biological or molecular
mechanisms underlying this association, or discuss studies in other cancers that have explored
how ulceration might promote lymph node metastasis (e.g., through inflammation or tumor-
induced immune suppression). This could pave the way for future studies examining the biology
of LNM in CC.
Title: Predicting the risk of lymph node metastasis in colon cancer: development and validation
of an online dynamic nomogram based on multiple preoperative data
Version: 2 Date: 14 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the introduction discusses various factors (e.g., symptoms, tumor markers), it would
benefit from specifying some of the key clinical or laboratory factors that are anticipated to be
significant in the prediction of LNM, offering a clearer view of the model's predictors.
The introduction generalizes some points about CRC research but does not provide specific
examples of studies that might have successfully addressed LNM prediction or those that fell
short, which could help contextualize the gap more specifically.
-Although excluding patients with missing data or high missing rates is reasonable, this approach
might lead to selection bias. It would be beneficial to explore if a sensitivity analysis or
alternative methods like multiple imputation could be used to mitigate potential biases arising
from missing data.
-While the use of LASSO helps mitigate overfitting, the large number of variables being
considered might still introduce complexity. Some justification of why all 21 variables are
necessary (and how each contributes to model performance) would enhance transparency.
The study includes endoscopic data as a predictor, but there is limited information on how
endoscopic findings are specifically incorporated into the model. Clarifying this could help
readers better understand the role of this factor.
-You clearly highlight the clinical importance of preoperative prediction for LNM, especially
in colon cancer (CC). Perhaps, a brief comparison with other existing tools/models for LNM
prediction would further emphasize the novelty and advantage of your model. For example,
you could mention if the nomogram's performance is significantly better than commonly used
clinical tools, such as the TNM staging system, in terms of accuracy, usability, or clinical utility.
-You speculated that tumor ulcers are correlated with increased LNM in CC patients, but also
mentioned that the exact mechanism remains unclear. Expanding this hypothesis could make
the discussion even more insightful. You could mention the potential biological or molecular
mechanisms underlying this association, or discuss studies in other cancers that have explored
how ulceration might promote lymph node metastasis (e.g., through inflammation or tumor-
induced immune suppression). This could pave the way for future studies examining the biology
of LNM in CC.
Please review this paper.
user
Title: Predicting the risk of lymph node metastasis in colon cancer: development and validation
of an online dynamic nomogram based on multiple preoperative data
Version: 2 Date: 14 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the introduction discusses various factors (e.g., symptoms, tumor markers), it would
benefit from specifying some of the key clinical or laboratory factors that are anticipated to be
significant in the prediction of LNM, offering a clearer view of the model's predictors.
The introduction generalizes some points about CRC research but does not provide specific
examples of studies that might have successfully addressed LNM prediction or those that fell
short, which could help contextualize the gap more specifically.
-Although excluding patients with missing data or high missing rates is reasonable, this approach
might lead to selection bias. It would be beneficial to explore if a sensitivity analysis or
alternative methods like multiple imputation could be used to mitigate potential biases arising
from missing data.
-While the use of LASSO helps mitigate overfitting, the large number of variables being
considered might still introduce complexity. Some justification of why all 21 variables are
necessary (and how each contributes to model performance) would enhance transparency.
The study includes endoscopic data as a predictor, but there is limited information on how
endoscopic findings are specifically incorporated into the model. Clarifying this could help
readers better understand the role of this factor.
-You clearly highlight the clinical importance of preoperative prediction for LNM, especially
in colon cancer (CC). Perhaps, a brief comparison with other existing tools/models for LNM
prediction would further emphasize the novelty and advantage of your model. For example,
you could mention if the nomogram's performance is significantly better than commonly used
clinical tools, such as the TNM staging system, in terms of accuracy, usability, or clinical utility.
-You speculated that tumor ulcers are correlated with increased LNM in CC patients, but also
mentioned that the exact mechanism remains unclear. Expanding this hypothesis could make
the discussion even more insightful. You could mention the potential biological or molecular
mechanisms underlying this association, or discuss studies in other cancers that have explored
how ulceration might promote lymph node metastasis (e.g., through inflammation or tumor-
induced immune suppression). This could pave the way for future studies examining the biology
of LNM in CC.
assistant