Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 5 Date: 21 Apr 2025
Reviewer's report:
This study presents a novel and promising framework for esophageal cancer grading by
integrating CT imaging, radiomics and deep learning. The innovative combination of multiple
techniques shows great potential for improving grading accuracy. The research has clear clinical
significance and provides a new direction for related studies, demonstrating high academic and
application value.
--In Figure 1, please provide the number of patients for each item in the inclusion and exclusion
process. Additionally, specify the number of patients in the training, validation, and testing
cohorts.
--For Table 1, conduct separate statistical analyses for the three cohorts. Further, stratify the data
into high - grade and low - grade categories to align with the classification task.
--The content regarding the Intra - Class Correlation Coefficient (ICC) evaluation of features is
excessive, causing the overall narrative to appear disorganized. It is recommended to move some
of the results to the supplementary materials and only present the key information. The focus of
the article should be on comparing the performance of radiomics and deep learning models.
--On page 13, please provide the corresponding website for the SERA software.
--The formula on page 14 can be removed.
--The Receiver Operating Characteristic (ROC) curve for the optimal model needs to be
presented.
--The activation maps on page 33 were selected . However, it is noted that the areas of focus by
the model mostly fall outside the tumor region.
Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 5 Date: 21 Apr 2025
Reviewer's report:
This study presents a novel and promising framework for esophageal cancer grading by
integrating CT imaging, radiomics and deep learning. The innovative combination of multiple
techniques shows great potential for improving grading accuracy. The research has clear clinical
significance and provides a new direction for related studies, demonstrating high academic and
application value.
--In Figure 1, please provide the number of patients for each item in the inclusion and exclusion
process. Additionally, specify the number of patients in the training, validation, and testing
cohorts.
--For Table 1, conduct separate statistical analyses for the three cohorts. Further, stratify the data
into high - grade and low - grade categories to align with the classification task.
--The content regarding the Intra - Class Correlation Coefficient (ICC) evaluation of features is
excessive, causing the overall narrative to appear disorganized. It is recommended to move some
of the results to the supplementary materials and only present the key information. The focus of
the article should be on comparing the performance of radiomics and deep learning models.
--On page 13, please provide the corresponding website for the SERA software.
--The formula on page 14 can be removed.
--The Receiver Operating Characteristic (ROC) curve for the optimal model needs to be
presented.
--The activation maps on page 33 were selected . However, it is noted that the areas of focus by
the model mostly fall outside the tumor region.
Please review this paper.
user
Title: A Novel Framework for Esophageal Cancer Grading: Combining CT Imaging,
Radiomics, Reproducibility, and Deep Learning Insights
Version: 5 Date: 21 Apr 2025
Reviewer's report:
This study presents a novel and promising framework for esophageal cancer grading by
integrating CT imaging, radiomics and deep learning. The innovative combination of multiple
techniques shows great potential for improving grading accuracy. The research has clear clinical
significance and provides a new direction for related studies, demonstrating high academic and
application value.
--In Figure 1, please provide the number of patients for each item in the inclusion and exclusion
process. Additionally, specify the number of patients in the training, validation, and testing
cohorts.
--For Table 1, conduct separate statistical analyses for the three cohorts. Further, stratify the data
into high - grade and low - grade categories to align with the classification task.
--The content regarding the Intra - Class Correlation Coefficient (ICC) evaluation of features is
excessive, causing the overall narrative to appear disorganized. It is recommended to move some
of the results to the supplementary materials and only present the key information. The focus of
the article should be on comparing the performance of radiomics and deep learning models.
--On page 13, please provide the corresponding website for the SERA software.
--The formula on page 14 can be removed.
--The Receiver Operating Characteristic (ROC) curve for the optimal model needs to be
presented.
--The activation maps on page 33 were selected . However, it is noted that the areas of focus by
the model mostly fall outside the tumor region.
assistant