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: 12 Jun 2024
Reviewer's report:
The author has perfectly addressed the majority of concerns. Nonetheless, there are still several
minor aspects should be solved.
1. The addition of Model Comparison appears somewhat weird in its current placement. It is
more advisable to place into the Construction of Machine Learning Models section.
2. In the results section, it is recommended to present only the findings, with descriptions
involving methods and processes placed in the Materials and Methods section.
3. The author has offered an exhaustive clarification concerning the high-resolution T2 sequence.
Yet, the parameter settings currently do not convincingly demonstrate the high-resolution nature
of the sequence.
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: 12 Jun 2024
Reviewer's report:
The author has perfectly addressed the majority of concerns. Nonetheless, there are still several
minor aspects should be solved.
1. The addition of Model Comparison appears somewhat weird in its current placement. It is
more advisable to place into the Construction of Machine Learning Models section.
2. In the results section, it is recommended to present only the findings, with descriptions
involving methods and processes placed in the Materials and Methods section.
3. The author has offered an exhaustive clarification concerning the high-resolution T2 sequence.
Yet, the parameter settings currently do not convincingly demonstrate the high-resolution nature
of the sequence.
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: 12 Jun 2024
Reviewer's report:
The author has perfectly addressed the majority of concerns. Nonetheless, there are still several
minor aspects should be solved.
1. The addition of Model Comparison appears somewhat weird in its current placement. It is
more advisable to place into the Construction of Machine Learning Models section.
2. In the results section, it is recommended to present only the findings, with descriptions
involving methods and processes placed in the Materials and Methods section.
3. The author has offered an exhaustive clarification concerning the high-resolution T2 sequence.
Yet, the parameter settings currently do not convincingly demonstrate the high-resolution nature
of the sequence.
assistant