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: 1 Date: 13 Jan 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Including a larger and more diverse external cohort would enhance the model's generalizability.
It would be beneficial to test the nomogram in multiple centers, ideally in both academic and
community hospital settings.
-A more detailed discussion of handling missing data, including a justification for excluding
patients with missing data, would strengthen the study. The authors could also consider
exploring imputation techniques for missing values to minimize potential biases.
-The usability and integration of the web-based dynamic nomogram should be evaluated in real-
world clinical settings. It would be helpful to include feedback from clinicians regarding the
tool’s practicality, user-friendliness, and time efficiency.
-A comparison of this nomogram with other existing LNM prediction models for colon cancer
would provide a clearer context for its performance and demonstrate its relative advantage over
other tools. This would help position the nomogram as a novel contribution to the field.
-The discussion briefly acknowledges the retrospective nature of the study, which is a limitation.
However, there is insufficient detail on how the authors minimized or controlled biases
inherent to retrospective studies (e.g., selection bias, information bias). A more comprehensive
explanation of these efforts would strengthen the interpretation of the results and help readers
understand the reliability of the model.
-The authors mention the reclassification of continuous variables into categorical ones,
acknowledging the potential loss of information. This is a significant limitation that could
reduce the model’s predictive power. While this decision may have been necessary for model
construction, the discussion could benefit from further exploration of alternative approaches,
such as using continuous variables directly in the model or justifying the choice of categorization
with a stronger rationale.
-While the model was validated internally and in an external cohort, the authors recognize the
need for further external validation in diverse medical centers. The discussion could be more
explicit about the limitations of the external validation cohort, especially regarding its size (112
patients) and potential biases due to population differences between the training and validation
sets.
-The suggestion to use additional external cohorts to assess the model’s performance across
different regions and hospital settings is sound, but a clearer outline of the steps for this validation
process would provide more confidence in the model’s generalizability.
-Although the nomogram includes a comprehensive range of variables, the discussion could
briefly touch on whether any potentially important clinical factors were excluded from the
analysis, such as genetic or molecular biomarkers (e.g., KRAS, BRAF mutations). Including
molecular data might improve the predictive accuracy of the model in the future, and it would
be worth mentioning these possibilities.
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: 1 Date: 13 Jan 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Including a larger and more diverse external cohort would enhance the model's generalizability.
It would be beneficial to test the nomogram in multiple centers, ideally in both academic and
community hospital settings.
-A more detailed discussion of handling missing data, including a justification for excluding
patients with missing data, would strengthen the study. The authors could also consider
exploring imputation techniques for missing values to minimize potential biases.
-The usability and integration of the web-based dynamic nomogram should be evaluated in real-
world clinical settings. It would be helpful to include feedback from clinicians regarding the
tool’s practicality, user-friendliness, and time efficiency.
-A comparison of this nomogram with other existing LNM prediction models for colon cancer
would provide a clearer context for its performance and demonstrate its relative advantage over
other tools. This would help position the nomogram as a novel contribution to the field.
-The discussion briefly acknowledges the retrospective nature of the study, which is a limitation.
However, there is insufficient detail on how the authors minimized or controlled biases
inherent to retrospective studies (e.g., selection bias, information bias). A more comprehensive
explanation of these efforts would strengthen the interpretation of the results and help readers
understand the reliability of the model.
-The authors mention the reclassification of continuous variables into categorical ones,
acknowledging the potential loss of information. This is a significant limitation that could
reduce the model’s predictive power. While this decision may have been necessary for model
construction, the discussion could benefit from further exploration of alternative approaches,
such as using continuous variables directly in the model or justifying the choice of categorization
with a stronger rationale.
-While the model was validated internally and in an external cohort, the authors recognize the
need for further external validation in diverse medical centers. The discussion could be more
explicit about the limitations of the external validation cohort, especially regarding its size (112
patients) and potential biases due to population differences between the training and validation
sets.
-The suggestion to use additional external cohorts to assess the model’s performance across
different regions and hospital settings is sound, but a clearer outline of the steps for this validation
process would provide more confidence in the model’s generalizability.
-Although the nomogram includes a comprehensive range of variables, the discussion could
briefly touch on whether any potentially important clinical factors were excluded from the
analysis, such as genetic or molecular biomarkers (e.g., KRAS, BRAF mutations). Including
molecular data might improve the predictive accuracy of the model in the future, and it would
be worth mentioning these possibilities.
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: 1 Date: 13 Jan 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Including a larger and more diverse external cohort would enhance the model's generalizability.
It would be beneficial to test the nomogram in multiple centers, ideally in both academic and
community hospital settings.
-A more detailed discussion of handling missing data, including a justification for excluding
patients with missing data, would strengthen the study. The authors could also consider
exploring imputation techniques for missing values to minimize potential biases.
-The usability and integration of the web-based dynamic nomogram should be evaluated in real-
world clinical settings. It would be helpful to include feedback from clinicians regarding the
tool’s practicality, user-friendliness, and time efficiency.
-A comparison of this nomogram with other existing LNM prediction models for colon cancer
would provide a clearer context for its performance and demonstrate its relative advantage over
other tools. This would help position the nomogram as a novel contribution to the field.
-The discussion briefly acknowledges the retrospective nature of the study, which is a limitation.
However, there is insufficient detail on how the authors minimized or controlled biases
inherent to retrospective studies (e.g., selection bias, information bias). A more comprehensive
explanation of these efforts would strengthen the interpretation of the results and help readers
understand the reliability of the model.
-The authors mention the reclassification of continuous variables into categorical ones,
acknowledging the potential loss of information. This is a significant limitation that could
reduce the model’s predictive power. While this decision may have been necessary for model
construction, the discussion could benefit from further exploration of alternative approaches,
such as using continuous variables directly in the model or justifying the choice of categorization
with a stronger rationale.
-While the model was validated internally and in an external cohort, the authors recognize the
need for further external validation in diverse medical centers. The discussion could be more
explicit about the limitations of the external validation cohort, especially regarding its size (112
patients) and potential biases due to population differences between the training and validation
sets.
-The suggestion to use additional external cohorts to assess the model’s performance across
different regions and hospital settings is sound, but a clearer outline of the steps for this validation
process would provide more confidence in the model’s generalizability.
-Although the nomogram includes a comprehensive range of variables, the discussion could
briefly touch on whether any potentially important clinical factors were excluded from the
analysis, such as genetic or molecular biomarkers (e.g., KRAS, BRAF mutations). Including
molecular data might improve the predictive accuracy of the model in the future, and it would
be worth mentioning these possibilities.
assistant