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: 1 Date: 12 Apr 2024
Reviewer's report:
The study first uses ResNet34 and PyRadiomics for features extraction, then uses LR, RF, DT
and SVM for the classification of Stage T2 and T3 for rectal cancer.
The study extracts 2048 features from the Average Pooling layer, but the layer with 2048 features
has not been found in Fig.3. Is it between (conv, 512) and (fc, 1000)? How was 2048 calculated?
24 features were preserved by LASSO. Are all these 24 features included in LR, RF, DT and
SVM models? Please show the structure of the decision trees for RF and DT methods. Please
add more details of the SVM model. For example, Gamma and C parameters. Does SVM model
also include 24 features?
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: 1 Date: 12 Apr 2024
Reviewer's report:
The study first uses ResNet34 and PyRadiomics for features extraction, then uses LR, RF, DT
and SVM for the classification of Stage T2 and T3 for rectal cancer.
The study extracts 2048 features from the Average Pooling layer, but the layer with 2048 features
has not been found in Fig.3. Is it between (conv, 512) and (fc, 1000)? How was 2048 calculated?
24 features were preserved by LASSO. Are all these 24 features included in LR, RF, DT and
SVM models? Please show the structure of the decision trees for RF and DT methods. Please
add more details of the SVM model. For example, Gamma and C parameters. Does SVM model
also include 24 features?
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: 1 Date: 12 Apr 2024
Reviewer's report:
The study first uses ResNet34 and PyRadiomics for features extraction, then uses LR, RF, DT
and SVM for the classification of Stage T2 and T3 for rectal cancer.
The study extracts 2048 features from the Average Pooling layer, but the layer with 2048 features
has not been found in Fig.3. Is it between (conv, 512) and (fc, 1000)? How was 2048 calculated?
24 features were preserved by LASSO. Are all these 24 features included in LR, RF, DT and
SVM models? Please show the structure of the decision trees for RF and DT methods. Please
add more details of the SVM model. For example, Gamma and C parameters. Does SVM model
also include 24 features?
assistant