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: 28 Apr 2024
Reviewer's report:
This study cleverly integrates MRI-based deep transfer learning techniques with radiomic
features, employing four different machine learning algorithms (LR, RF, DT, and SVM) to
develop predictive models. Clinical risk factors are integrated into the predictive framework,
creating a comprehensive model capable of accurately predicting the T2 and T3 staging of
colorectal cancer in a non-invasive manner preoperatively. The study is intriguing and holds
significant practical value, yet there are several noteworthy aspects to consider.
1. The primary innovation lies in employing the ResNet-34 model augmented with residual
connections for feature extraction through deep transfer learning. However, both the Results
and Discussion sections lack comparisons with conventional methods, necessitating such
comparisons to validate the study's design for effectively enhancing predictive performance over
conventional methods.
2. Why the setting of high-resolution T2 layer thickness and spacing of 3mm and 1mm needs
clarification.
3. ROI delineation.  Detailed explanations of "two seasoned radiologists" are required.  A
detailed description of 3D delineation is warranted. Figure 2 is suggested to feature typical cases
of rectal cancer.
4. More technical details should be provided regarding data processing and feature selection
methods.
5. The setting of cut-off values for CEA and CA199 needs elucidation.
6. There is a lack of in-depth discussion surrounding the study results. Specifically: 1. Why
deep transfer learning features exhibit significantly greater significance compared to radiomic
features. 2. LD and CEA are independent predictors, but why isn't CA199? 3. Why SVM
outperforms other models? 4. Whether the comprehensive model shows improvement over other
studies and the underlying reasons for such analysis.
7. Some figures lack annotations.
8. Abbreviations within the text lack precision.
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: 28 Apr 2024
Reviewer's report:
This study cleverly integrates MRI-based deep transfer learning techniques with radiomic
features, employing four different machine learning algorithms (LR, RF, DT, and SVM) to
develop predictive models. Clinical risk factors are integrated into the predictive framework,
creating a comprehensive model capable of accurately predicting the T2 and T3 staging of
colorectal cancer in a non-invasive manner preoperatively. The study is intriguing and holds
significant practical value, yet there are several noteworthy aspects to consider.
1. The primary innovation lies in employing the ResNet-34 model augmented with residual
connections for feature extraction through deep transfer learning. However, both the Results
and Discussion sections lack comparisons with conventional methods, necessitating such
comparisons to validate the study's design for effectively enhancing predictive performance over
conventional methods.
2. Why the setting of high-resolution T2 layer thickness and spacing of 3mm and 1mm needs
clarification.
3. ROI delineation.  Detailed explanations of "two seasoned radiologists" are required.  A
detailed description of 3D delineation is warranted. Figure 2 is suggested to feature typical cases
of rectal cancer.
4. More technical details should be provided regarding data processing and feature selection
methods.
5. The setting of cut-off values for CEA and CA199 needs elucidation.
6. There is a lack of in-depth discussion surrounding the study results. Specifically: 1. Why
deep transfer learning features exhibit significantly greater significance compared to radiomic
features. 2. LD and CEA are independent predictors, but why isn't CA199? 3. Why SVM
outperforms other models? 4. Whether the comprehensive model shows improvement over other
studies and the underlying reasons for such analysis.
7. Some figures lack annotations.
8. Abbreviations within the text lack precision.
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: 28 Apr 2024
Reviewer's report:
This study cleverly integrates MRI-based deep transfer learning techniques with radiomic
features, employing four different machine learning algorithms (LR, RF, DT, and SVM) to
develop predictive models. Clinical risk factors are integrated into the predictive framework,
creating a comprehensive model capable of accurately predicting the T2 and T3 staging of
colorectal cancer in a non-invasive manner preoperatively. The study is intriguing and holds
significant practical value, yet there are several noteworthy aspects to consider.
1. The primary innovation lies in employing the ResNet-34 model augmented with residual
connections for feature extraction through deep transfer learning. However, both the Results
and Discussion sections lack comparisons with conventional methods, necessitating such
comparisons to validate the study's design for effectively enhancing predictive performance over
conventional methods.
2. Why the setting of high-resolution T2 layer thickness and spacing of 3mm and 1mm needs
clarification.
3. ROI delineation.  Detailed explanations of "two seasoned radiologists" are required.  A
detailed description of 3D delineation is warranted. Figure 2 is suggested to feature typical cases
of rectal cancer.
4. More technical details should be provided regarding data processing and feature selection
methods.
5. The setting of cut-off values for CEA and CA199 needs elucidation.
6. There is a lack of in-depth discussion surrounding the study results. Specifically: 1. Why
deep transfer learning features exhibit significantly greater significance compared to radiomic
features. 2. LD and CEA are independent predictors, but why isn't CA199? 3. Why SVM
outperforms other models? 4. Whether the comprehensive model shows improvement over other
studies and the underlying reasons for such analysis.
7. Some figures lack annotations.
8. Abbreviations within the text lack precision.
assistant