Title: The CT-based intratumoral and peritumoral machine learning radiomics analysis in
predicting lymph node metastasis in rectal carcinoma
Version: 1 Date: 04 Sep 2022
Reviewer's report:
The paper demonstrated that the preoperative assessment of lymph node metastasis (LNM) plays
an important role in adjuvant therapy as well as surgery, but current radiological techniques
lacking consensus on a standard definition of LNM limited its diagnostic accuracy. The author
proposed that the intratumoral and peritumoral radiomics based Bayes analysis can help predict
the LNM status of RCs and the bayes analysis. The paper is generally clearly written but missing
many technical details. Some points below could be addressed to improve the manuscript.
1. In discussion section, the author argued that their study focused on the radiomics-based
machine learning to predict the LNM status of RCs, then the logic of using clinical-Bayes
nomogram which can provide a better production is unclear.
2. The best AUC of radiomics-based machine learning methods is only around 0.65, which is
not a good discrimination in classification. Also, the results from other comparison methods,
e.g., KNN, LR, SVM and DT, are missing.
3. In Figure 1, the author mentioned the ten-fold cross-validation (data randomly partitioned into
10 parts, 9 parts used for training and the rest 1 for testing), but the paper used 7:3 and didn’t
explain how to do the cross-validation.
4. The author mentioned that they use GBDT methods to select the features, but they didn’t
provide the ranking of the features in the paper and supplementary methods, even not a total
number of features. It is better to see how the parameters are set and whether the features selected
are meaningful.
5. The author mentioned that to improve the predictive efficacy, the significant clinical factors
were considered. However, they didn’t explain how they select those features and whether they
use the clinical features and radiomics features together. If clinical-Bayes nomogram can achieve
much higher AUC, then whether the radiomics-based methods are meaningful.
6. No code in the provided github.
7. It is better to add more explanations for the figures, e.g., Figure 4, how to read the figure,
what is the x-axis and y-axis represent.
Title: The CT-based intratumoral and peritumoral machine learning radiomics analysis in
predicting lymph node metastasis in rectal carcinoma
Version: 1 Date: 04 Sep 2022
Reviewer's report:
The paper demonstrated that the preoperative assessment of lymph node metastasis (LNM) plays
an important role in adjuvant therapy as well as surgery, but current radiological techniques
lacking consensus on a standard definition of LNM limited its diagnostic accuracy. The author
proposed that the intratumoral and peritumoral radiomics based Bayes analysis can help predict
the LNM status of RCs and the bayes analysis. The paper is generally clearly written but missing
many technical details. Some points below could be addressed to improve the manuscript.
1. In discussion section, the author argued that their study focused on the radiomics-based
machine learning to predict the LNM status of RCs, then the logic of using clinical-Bayes
nomogram which can provide a better production is unclear.
2. The best AUC of radiomics-based machine learning methods is only around 0.65, which is
not a good discrimination in classification. Also, the results from other comparison methods,
e.g., KNN, LR, SVM and DT, are missing.
3. In Figure 1, the author mentioned the ten-fold cross-validation (data randomly partitioned into
10 parts, 9 parts used for training and the rest 1 for testing), but the paper used 7:3 and didn’t
explain how to do the cross-validation.
4. The author mentioned that they use GBDT methods to select the features, but they didn’t
provide the ranking of the features in the paper and supplementary methods, even not a total
number of features. It is better to see how the parameters are set and whether the features selected
are meaningful.
5. The author mentioned that to improve the predictive efficacy, the significant clinical factors
were considered. However, they didn’t explain how they select those features and whether they
use the clinical features and radiomics features together. If clinical-Bayes nomogram can achieve
much higher AUC, then whether the radiomics-based methods are meaningful.
6. No code in the provided github.
7. It is better to add more explanations for the figures, e.g., Figure 4, how to read the figure,
what is the x-axis and y-axis represent.
Please review this paper.
user
Title: The CT-based intratumoral and peritumoral machine learning radiomics analysis in
predicting lymph node metastasis in rectal carcinoma
Version: 1 Date: 04 Sep 2022
Reviewer's report:
The paper demonstrated that the preoperative assessment of lymph node metastasis (LNM) plays
an important role in adjuvant therapy as well as surgery, but current radiological techniques
lacking consensus on a standard definition of LNM limited its diagnostic accuracy. The author
proposed that the intratumoral and peritumoral radiomics based Bayes analysis can help predict
the LNM status of RCs and the bayes analysis. The paper is generally clearly written but missing
many technical details. Some points below could be addressed to improve the manuscript.
1. In discussion section, the author argued that their study focused on the radiomics-based
machine learning to predict the LNM status of RCs, then the logic of using clinical-Bayes
nomogram which can provide a better production is unclear.
2. The best AUC of radiomics-based machine learning methods is only around 0.65, which is
not a good discrimination in classification. Also, the results from other comparison methods,
e.g., KNN, LR, SVM and DT, are missing.
3. In Figure 1, the author mentioned the ten-fold cross-validation (data randomly partitioned into
10 parts, 9 parts used for training and the rest 1 for testing), but the paper used 7:3 and didn’t
explain how to do the cross-validation.
4. The author mentioned that they use GBDT methods to select the features, but they didn’t
provide the ranking of the features in the paper and supplementary methods, even not a total
number of features. It is better to see how the parameters are set and whether the features selected
are meaningful.
5. The author mentioned that to improve the predictive efficacy, the significant clinical factors
were considered. However, they didn’t explain how they select those features and whether they
use the clinical features and radiomics features together. If clinical-Bayes nomogram can achieve
much higher AUC, then whether the radiomics-based methods are meaningful.
6. No code in the provided github.
7. It is better to add more explanations for the figures, e.g., Figure 4, how to read the figure,
what is the x-axis and y-axis represent.
assistant