Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 3 Date: 07 Feb 2025
Reviewer:  Fei Zheng
Reviewer's report:
Comments: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Innovation:
1. The study presents a novel approach to esophageal cancer grading by integrating radiomic
features with deep learning techniques, enhanced by attention mechanisms. This approach is
promising and demonstrates potential in improving grading accuracy.
2. The multi-segmentation strategy employed in this study enhances the reproducibility of the
extracted features, ensuring the robustness of the results and minimizing variability.
Areas for Improvement:
1. Clinical Content and Significance: The clinical relevance of the study should be more
thoroughly discussed. Although the article briefly touches on this in the first paragraph, further
elaboration is necessary. Specifically, the impact of early esophageal cancer grading on patient
outcomes should be detailed. Additionally, the classification of esophageal cancer based on
its anatomical location (upper, middle, or lower esophagus) needs clarification, especially
regarding the specific anatomical regions selected for this study.
2. Data Collection and Multicenter Data: The study involves a large dataset, including 2,560
patients diagnosed with esophageal cancer from multiple clinical centers (2018–2023). It would
be beneficial to mention the names of the participating centers to ensure transparency and allow
for a clearer understanding of the diversity of the data.
3. Excessive Length and Repetition:
a) The manuscript is lengthy, with several repeated sentences. While repetition may be intended
to emphasize key points, some of it can be reduced.
b) Certain sections, such as the inclusion and exclusion criteria, can be condensed into a
single sentence, and a flowchart of the inclusion/exclusion criteria can be provided in the
supplementary materials.
c) The types and numbers of radiomic features extracted could be summarized in a table and
placed in the supplementary materials to enhance clarity.
4. Clinical and Demographic Data: “Full clinical and demographic information was also
required, enabling a robust correlation between imaging features and patient-specific
characteristics”. However, no correlation analysis is conducted between imaging features and
clinical data in the manuscript. The authors are encouraged to provide such analysis or adjust
the manuscript accordingly.
5. Table 1: Clinical Features and Statistical Significance: It would be helpful if Table 1 could
show the differences in clinical features across various levels of esophageal cancer, with
statistical significance values included.
6. Figure 1: The article presents two feature extraction methods—Radiomic Feature Extraction
and Attention-Enhanced Deep Learning. These methods should be shown as parallel processes,
rather than in a sequential order.
7. Preprocessing Stage: The description of the preprocessing stage could be more detailed. “This
included smoothing the tumor boundaries to remove irregularities and aligning the segmented
slices across different imaging planes”. Providing relevant code could further support the
reproducibility of the study.
8. Figure Legends: The figure legends, particularly for Figure 2, are too simple. While the content
is discussed in the article, it would be beneficial to provide a more detailed explanation in the
figure legends, especially regarding the relationships between the components of the figure. For
Figure 2, is the relationship between the components progressive or hierarchical? Additionally,
the description of the ensemble model is unclear. The manuscript mentions a fusion of two
models, but the figure suggests three. Please verify and update accordingly.
9. Figures 7 and 8: Based on the content of the article, Figures 7 and 8 depict feature
extraction using different deep learning models (DenseNet121, EfficientNet-B0), followed by
RFE and PCA for feature selection, and the use of XGBoost, CatBoost, and Random Forest
classifiers for modeling. The current labeling in the figure (Classification Using EfficientNet-
B0/DenseNet121) is misleading. It would be clearer to label these as "Feature Extraction Using
EfficientNet-B0/DenseNet121." The same clarification applies to Figure 9.
10. Training, Validation, and Testing: The manuscript mentions that the integrated feature set
was split into three subsets: 70% for training, 15% for validation, and 15% for testing. However,
it seems that the results are only provided for the validation subset. It would be beneficial to
provide a table containing the results for the training, validation, and testing subsets for a more
comprehensive evaluation of model performance.
Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 3 Date: 07 Feb 2025
Reviewer:  Fei Zheng
Reviewer's report:
Comments: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Innovation:
1. The study presents a novel approach to esophageal cancer grading by integrating radiomic
features with deep learning techniques, enhanced by attention mechanisms. This approach is
promising and demonstrates potential in improving grading accuracy.
2. The multi-segmentation strategy employed in this study enhances the reproducibility of the
extracted features, ensuring the robustness of the results and minimizing variability.
Areas for Improvement:
1. Clinical Content and Significance: The clinical relevance of the study should be more
thoroughly discussed. Although the article briefly touches on this in the first paragraph, further
elaboration is necessary. Specifically, the impact of early esophageal cancer grading on patient
outcomes should be detailed. Additionally, the classification of esophageal cancer based on
its anatomical location (upper, middle, or lower esophagus) needs clarification, especially
regarding the specific anatomical regions selected for this study.
2. Data Collection and Multicenter Data: The study involves a large dataset, including 2,560
patients diagnosed with esophageal cancer from multiple clinical centers (2018–2023). It would
be beneficial to mention the names of the participating centers to ensure transparency and allow
for a clearer understanding of the diversity of the data.
3. Excessive Length and Repetition:
a) The manuscript is lengthy, with several repeated sentences. While repetition may be intended
to emphasize key points, some of it can be reduced.
b) Certain sections, such as the inclusion and exclusion criteria, can be condensed into a
single sentence, and a flowchart of the inclusion/exclusion criteria can be provided in the
supplementary materials.
c) The types and numbers of radiomic features extracted could be summarized in a table and
placed in the supplementary materials to enhance clarity.
4. Clinical and Demographic Data: “Full clinical and demographic information was also
required, enabling a robust correlation between imaging features and patient-specific
characteristics”. However, no correlation analysis is conducted between imaging features and
clinical data in the manuscript. The authors are encouraged to provide such analysis or adjust
the manuscript accordingly.
5. Table 1: Clinical Features and Statistical Significance: It would be helpful if Table 1 could
show the differences in clinical features across various levels of esophageal cancer, with
statistical significance values included.
6. Figure 1: The article presents two feature extraction methods—Radiomic Feature Extraction
and Attention-Enhanced Deep Learning. These methods should be shown as parallel processes,
rather than in a sequential order.
7. Preprocessing Stage: The description of the preprocessing stage could be more detailed. “This
included smoothing the tumor boundaries to remove irregularities and aligning the segmented
slices across different imaging planes”. Providing relevant code could further support the
reproducibility of the study.
8. Figure Legends: The figure legends, particularly for Figure 2, are too simple. While the content
is discussed in the article, it would be beneficial to provide a more detailed explanation in the
figure legends, especially regarding the relationships between the components of the figure. For
Figure 2, is the relationship between the components progressive or hierarchical? Additionally,
the description of the ensemble model is unclear. The manuscript mentions a fusion of two
models, but the figure suggests three. Please verify and update accordingly.
9. Figures 7 and 8: Based on the content of the article, Figures 7 and 8 depict feature
extraction using different deep learning models (DenseNet121, EfficientNet-B0), followed by
RFE and PCA for feature selection, and the use of XGBoost, CatBoost, and Random Forest
classifiers for modeling. The current labeling in the figure (Classification Using EfficientNet-
B0/DenseNet121) is misleading. It would be clearer to label these as "Feature Extraction Using
EfficientNet-B0/DenseNet121." The same clarification applies to Figure 9.
10. Training, Validation, and Testing: The manuscript mentions that the integrated feature set
was split into three subsets: 70% for training, 15% for validation, and 15% for testing. However,
it seems that the results are only provided for the validation subset. It would be beneficial to
provide a table containing the results for the training, validation, and testing subsets for a more
comprehensive evaluation of model performance.
Please review this paper.
user
Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 3 Date: 07 Feb 2025
Reviewer:  Fei Zheng
Reviewer's report:
Comments: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Innovation:
1. The study presents a novel approach to esophageal cancer grading by integrating radiomic
features with deep learning techniques, enhanced by attention mechanisms. This approach is
promising and demonstrates potential in improving grading accuracy.
2. The multi-segmentation strategy employed in this study enhances the reproducibility of the
extracted features, ensuring the robustness of the results and minimizing variability.
Areas for Improvement:
1. Clinical Content and Significance: The clinical relevance of the study should be more
thoroughly discussed. Although the article briefly touches on this in the first paragraph, further
elaboration is necessary. Specifically, the impact of early esophageal cancer grading on patient
outcomes should be detailed. Additionally, the classification of esophageal cancer based on
its anatomical location (upper, middle, or lower esophagus) needs clarification, especially
regarding the specific anatomical regions selected for this study.
2. Data Collection and Multicenter Data: The study involves a large dataset, including 2,560
patients diagnosed with esophageal cancer from multiple clinical centers (2018–2023). It would
be beneficial to mention the names of the participating centers to ensure transparency and allow
for a clearer understanding of the diversity of the data.
3. Excessive Length and Repetition:
a) The manuscript is lengthy, with several repeated sentences. While repetition may be intended
to emphasize key points, some of it can be reduced.
b) Certain sections, such as the inclusion and exclusion criteria, can be condensed into a
single sentence, and a flowchart of the inclusion/exclusion criteria can be provided in the
supplementary materials.
c) The types and numbers of radiomic features extracted could be summarized in a table and
placed in the supplementary materials to enhance clarity.
4. Clinical and Demographic Data: “Full clinical and demographic information was also
required, enabling a robust correlation between imaging features and patient-specific
characteristics”. However, no correlation analysis is conducted between imaging features and
clinical data in the manuscript. The authors are encouraged to provide such analysis or adjust
the manuscript accordingly.
5. Table 1: Clinical Features and Statistical Significance: It would be helpful if Table 1 could
show the differences in clinical features across various levels of esophageal cancer, with
statistical significance values included.
6. Figure 1: The article presents two feature extraction methods—Radiomic Feature Extraction
and Attention-Enhanced Deep Learning. These methods should be shown as parallel processes,
rather than in a sequential order.
7. Preprocessing Stage: The description of the preprocessing stage could be more detailed. “This
included smoothing the tumor boundaries to remove irregularities and aligning the segmented
slices across different imaging planes”. Providing relevant code could further support the
reproducibility of the study.
8. Figure Legends: The figure legends, particularly for Figure 2, are too simple. While the content
is discussed in the article, it would be beneficial to provide a more detailed explanation in the
figure legends, especially regarding the relationships between the components of the figure. For
Figure 2, is the relationship between the components progressive or hierarchical? Additionally,
the description of the ensemble model is unclear. The manuscript mentions a fusion of two
models, but the figure suggests three. Please verify and update accordingly.
9. Figures 7 and 8: Based on the content of the article, Figures 7 and 8 depict feature
extraction using different deep learning models (DenseNet121, EfficientNet-B0), followed by
RFE and PCA for feature selection, and the use of XGBoost, CatBoost, and Random Forest
classifiers for modeling. The current labeling in the figure (Classification Using EfficientNet-
B0/DenseNet121) is misleading. It would be clearer to label these as "Feature Extraction Using
EfficientNet-B0/DenseNet121." The same clarification applies to Figure 9.
10. Training, Validation, and Testing: The manuscript mentions that the integrated feature set
was split into three subsets: 70% for training, 15% for validation, and 15% for testing. However,
it seems that the results are only provided for the validation subset. It would be beneficial to
provide a table containing the results for the training, validation, and testing subsets for a more
comprehensive evaluation of model performance.
assistant