Title: Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer
Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to
T3
Version: 2 Date: 02 Jun 2024
Reviewer's report:
Thanks for replying to my concerns about feature number and the structures of RF and DT.
Could you introduce the software you used to produce Fig A and Fig B in the response letter
about RF and DT models? What is meaning of ‘0’ and ‘1’ in the figure? Could you also introduce
the software and packages for the classification by LR, RF, DT and SVM?
The study selects SVM as the best classifier and includes 24 features. Is it possible to use an
equation or a diagram to quantitively show the SVM model, just like the equation in LR, or
diagrams in RF or DT?
Because non-linear kernel was used, too many features may lead to over-fitting. Therefore, an
independent external validation is preferred.
Fig 5B, does the dotted line correspond to 24 features? There seems to be only 5 or 6 non-zero
features in the figure.
Title: Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer
Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to
T3
Version: 2 Date: 02 Jun 2024
Reviewer's report:
Thanks for replying to my concerns about feature number and the structures of RF and DT.
Could you introduce the software you used to produce Fig A and Fig B in the response letter
about RF and DT models? What is meaning of ‘0’ and ‘1’ in the figure? Could you also introduce
the software and packages for the classification by LR, RF, DT and SVM?
The study selects SVM as the best classifier and includes 24 features. Is it possible to use an
equation or a diagram to quantitively show the SVM model, just like the equation in LR, or
diagrams in RF or DT?
Because non-linear kernel was used, too many features may lead to over-fitting. Therefore, an
independent external validation is preferred.
Fig 5B, does the dotted line correspond to 24 features? There seems to be only 5 or 6 non-zero
features in the figure.
Please review this paper.
user
Title: Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer
Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to
T3
Version: 2 Date: 02 Jun 2024
Reviewer's report:
Thanks for replying to my concerns about feature number and the structures of RF and DT.
Could you introduce the software you used to produce Fig A and Fig B in the response letter
about RF and DT models? What is meaning of ‘0’ and ‘1’ in the figure? Could you also introduce
the software and packages for the classification by LR, RF, DT and SVM?
The study selects SVM as the best classifier and includes 24 features. Is it possible to use an
equation or a diagram to quantitively show the SVM model, just like the equation in LR, or
diagrams in RF or DT?
Because non-linear kernel was used, too many features may lead to over-fitting. Therefore, an
independent external validation is preferred.
Fig 5B, does the dotted line correspond to 24 features? There seems to be only 5 or 6 non-zero
features in the figure.
assistant