Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 3 Date: 14 Jan 2025
Reviewer's report:
1. How were the thresholds for reliability categories (e.g., ICC values) determined, and are they
consistent with other studies?
2. Why were DenseNet121 and EfficientNet-B0 selected over other deep learning models?
Would additional comparisons strengthen the study?
3. Could you elaborate on the criteria used to determine the number of radiomic features retained
after reproducibility assessments?
4. Were there any inter-observer variability assessments conducted during the multi-
segmentation strategy?
5. Why were RFE and PCA chosen as dimensionality reduction techniques, and were alternative
methods considered?
6. Table 2 mentions high reliability for morphology features. Were these more influential in
classification compared to texture features?
7. The study used the SERA toolkit for feature extraction. Were its limitations considered, and
would alternative platforms yield comparable results?
Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 3 Date: 14 Jan 2025
Reviewer's report:
1. How were the thresholds for reliability categories (e.g., ICC values) determined, and are they
consistent with other studies?
2. Why were DenseNet121 and EfficientNet-B0 selected over other deep learning models?
Would additional comparisons strengthen the study?
3. Could you elaborate on the criteria used to determine the number of radiomic features retained
after reproducibility assessments?
4. Were there any inter-observer variability assessments conducted during the multi-
segmentation strategy?
5. Why were RFE and PCA chosen as dimensionality reduction techniques, and were alternative
methods considered?
6. Table 2 mentions high reliability for morphology features. Were these more influential in
classification compared to texture features?
7. The study used the SERA toolkit for feature extraction. Were its limitations considered, and
would alternative platforms yield comparable results?
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: 14 Jan 2025
Reviewer's report:
1. How were the thresholds for reliability categories (e.g., ICC values) determined, and are they
consistent with other studies?
2. Why were DenseNet121 and EfficientNet-B0 selected over other deep learning models?
Would additional comparisons strengthen the study?
3. Could you elaborate on the criteria used to determine the number of radiomic features retained
after reproducibility assessments?
4. Were there any inter-observer variability assessments conducted during the multi-
segmentation strategy?
5. Why were RFE and PCA chosen as dimensionality reduction techniques, and were alternative
methods considered?
6. Table 2 mentions high reliability for morphology features. Were these more influential in
classification compared to texture features?
7. The study used the SERA toolkit for feature extraction. Were its limitations considered, and
would alternative platforms yield comparable results?
assistant