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: 0 Date: 17 Nov 2024
Reviewer's report:
This article discusses a study on predicting the risk of lymph node metastasis (LNM) in colon
cancer (CC) patients. The research team developed and validated an online dynamic nomogram
based on preoperative data to estimate the risk of LNM in colorectal cancer patients, which
performed well overall. However, there are several limitations:
1. Although the model performs well in internal validation, external validation is essential to
assess its applicability and robustness in different populations or datasets. Without it, the model’s
generalizability and potential for wider application are limited.
2. The article does not thoroughly explain the quality control measures during data collection
and processing, such as data cleaning and handling of outliers. These details are crucial for
evaluating the reliability of the data and the credibility of the study results.
3. The study design and discussion of results do not sufficiently address potential biases, such as
selection bias, information bias, or confounding factors. A deeper analysis of these limitations
would help readers understand the results more comprehensively and provide direction for future
improvements.
4. Language and grammar issues:
i#Abbreviations (e.g., LNM, CT, ROC) should be spelled out the first time they appear with the
abbreviation in parentheses. This standard is not consistently followed, potentially confusing
readers unfamiliar with the terms.
ii#Some sentences are long and complex, which may hinder readability. For example: "The
Least Absolute Shrinkage and Selection Operator (LASSO) method was used to initially
screen predictors from 21 variables to better deal with the problem of model overfitting and
multicollinearity associated with high-dimensional samples."
iii: There are instances of unnecessary repetition. For example, "The nomogram was developed
to predict the risk of LNM in colon cancer patients. The nomogram is a tool that helps predict
the risk of LNM in colon cancer patients." Repetition should be avoided.
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: 0 Date: 17 Nov 2024
Reviewer's report:
This article discusses a study on predicting the risk of lymph node metastasis (LNM) in colon
cancer (CC) patients. The research team developed and validated an online dynamic nomogram
based on preoperative data to estimate the risk of LNM in colorectal cancer patients, which
performed well overall. However, there are several limitations:
1. Although the model performs well in internal validation, external validation is essential to
assess its applicability and robustness in different populations or datasets. Without it, the model’s
generalizability and potential for wider application are limited.
2. The article does not thoroughly explain the quality control measures during data collection
and processing, such as data cleaning and handling of outliers. These details are crucial for
evaluating the reliability of the data and the credibility of the study results.
3. The study design and discussion of results do not sufficiently address potential biases, such as
selection bias, information bias, or confounding factors. A deeper analysis of these limitations
would help readers understand the results more comprehensively and provide direction for future
improvements.
4. Language and grammar issues:
i#Abbreviations (e.g., LNM, CT, ROC) should be spelled out the first time they appear with the
abbreviation in parentheses. This standard is not consistently followed, potentially confusing
readers unfamiliar with the terms.
ii#Some sentences are long and complex, which may hinder readability. For example: "The
Least Absolute Shrinkage and Selection Operator (LASSO) method was used to initially
screen predictors from 21 variables to better deal with the problem of model overfitting and
multicollinearity associated with high-dimensional samples."
iii: There are instances of unnecessary repetition. For example, "The nomogram was developed
to predict the risk of LNM in colon cancer patients. The nomogram is a tool that helps predict
the risk of LNM in colon cancer patients." Repetition should be avoided.
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: 0 Date: 17 Nov 2024
Reviewer's report:
This article discusses a study on predicting the risk of lymph node metastasis (LNM) in colon
cancer (CC) patients. The research team developed and validated an online dynamic nomogram
based on preoperative data to estimate the risk of LNM in colorectal cancer patients, which
performed well overall. However, there are several limitations:
1. Although the model performs well in internal validation, external validation is essential to
assess its applicability and robustness in different populations or datasets. Without it, the model’s
generalizability and potential for wider application are limited.
2. The article does not thoroughly explain the quality control measures during data collection
and processing, such as data cleaning and handling of outliers. These details are crucial for
evaluating the reliability of the data and the credibility of the study results.
3. The study design and discussion of results do not sufficiently address potential biases, such as
selection bias, information bias, or confounding factors. A deeper analysis of these limitations
would help readers understand the results more comprehensively and provide direction for future
improvements.
4. Language and grammar issues:
i#Abbreviations (e.g., LNM, CT, ROC) should be spelled out the first time they appear with the
abbreviation in parentheses. This standard is not consistently followed, potentially confusing
readers unfamiliar with the terms.
ii#Some sentences are long and complex, which may hinder readability. For example: "The
Least Absolute Shrinkage and Selection Operator (LASSO) method was used to initially
screen predictors from 21 variables to better deal with the problem of model overfitting and
multicollinearity associated with high-dimensional samples."
iii: There are instances of unnecessary repetition. For example, "The nomogram was developed
to predict the risk of LNM in colon cancer patients. The nomogram is a tool that helps predict
the risk of LNM in colon cancer patients." Repetition should be avoided.
assistant