Title: Convolutional neural network-based system for endocytoscopic diagnosis of early gastric
cancer
Version: 2 Date: 03 Feb 2022
Reviewer's report:
The authors constructed an early stage gastric cancer diagnosis system by combining ECS with
CNN, and reported the usefulness of their system by comparing it with experienced endoscopists.
Although this is an interesting report of a first attempt, there are some concerns.
First of all, in the text, it is stated that “our CNN for ECS showed higher accuracy and specificity
in diagnosing EGC compared to trained endoscopists”, but I think the accuracy was not higher,
is that right? The accuracy of CNN was equal to that of endoscopists, but when examined lesion
by lesion, the accuracy of CNN was inferior to that of endoscopists. With these results, I feel
that the authors need to be a little more cautious in concluding that CNN is useful. How about
comparing it to Trainee, or showing the time advantage?
Second, it is unclear what non-cancerous gastric mucosa (NGM) refers to.
The authors state that all ECS images were retrospectively collected from patients who
underwent ECS for the diagnosis of EGC, but it is not clear whether NGM is the mucosa
surrounding the cancer, or a benign tumor mistaken for cancer.
Has the pathology of NGM been proven?
A specific description is recommended.
Third, how were the total of ECS images used for validation selected?
Were they consecutive cases over a period of time?
A specific explanation and mention of possible selection bias would be appreciated.
Fourth, the authors state in the abstract that The trained CNN required 7.0 s to analyze the test
dataset. However, this is not stated in the text.
The difference in time with the endoscopists should be stated.
Fifth, it is not clear in which comparison there is a statistical difference. The p-value should be
stated in the text of the table and results.
Title: Convolutional neural network-based system for endocytoscopic diagnosis of early gastric
cancer
Version: 2 Date: 03 Feb 2022
Reviewer's report:
The authors constructed an early stage gastric cancer diagnosis system by combining ECS with
CNN, and reported the usefulness of their system by comparing it with experienced endoscopists.
Although this is an interesting report of a first attempt, there are some concerns.
First of all, in the text, it is stated that “our CNN for ECS showed higher accuracy and specificity
in diagnosing EGC compared to trained endoscopists”, but I think the accuracy was not higher,
is that right? The accuracy of CNN was equal to that of endoscopists, but when examined lesion
by lesion, the accuracy of CNN was inferior to that of endoscopists. With these results, I feel
that the authors need to be a little more cautious in concluding that CNN is useful. How about
comparing it to Trainee, or showing the time advantage?
Second, it is unclear what non-cancerous gastric mucosa (NGM) refers to.
The authors state that all ECS images were retrospectively collected from patients who
underwent ECS for the diagnosis of EGC, but it is not clear whether NGM is the mucosa
surrounding the cancer, or a benign tumor mistaken for cancer.
Has the pathology of NGM been proven?
A specific description is recommended.
Third, how were the total of ECS images used for validation selected?
Were they consecutive cases over a period of time?
A specific explanation and mention of possible selection bias would be appreciated.
Fourth, the authors state in the abstract that The trained CNN required 7.0 s to analyze the test
dataset. However, this is not stated in the text.
The difference in time with the endoscopists should be stated.
Fifth, it is not clear in which comparison there is a statistical difference. The p-value should be
stated in the text of the table and results.
Please review this paper.
user
Title: Convolutional neural network-based system for endocytoscopic diagnosis of early gastric
cancer
Version: 2 Date: 03 Feb 2022
Reviewer's report:
The authors constructed an early stage gastric cancer diagnosis system by combining ECS with
CNN, and reported the usefulness of their system by comparing it with experienced endoscopists.
Although this is an interesting report of a first attempt, there are some concerns.
First of all, in the text, it is stated that “our CNN for ECS showed higher accuracy and specificity
in diagnosing EGC compared to trained endoscopists”, but I think the accuracy was not higher,
is that right? The accuracy of CNN was equal to that of endoscopists, but when examined lesion
by lesion, the accuracy of CNN was inferior to that of endoscopists. With these results, I feel
that the authors need to be a little more cautious in concluding that CNN is useful. How about
comparing it to Trainee, or showing the time advantage?
Second, it is unclear what non-cancerous gastric mucosa (NGM) refers to.
The authors state that all ECS images were retrospectively collected from patients who
underwent ECS for the diagnosis of EGC, but it is not clear whether NGM is the mucosa
surrounding the cancer, or a benign tumor mistaken for cancer.
Has the pathology of NGM been proven?
A specific description is recommended.
Third, how were the total of ECS images used for validation selected?
Were they consecutive cases over a period of time?
A specific explanation and mention of possible selection bias would be appreciated.
Fourth, the authors state in the abstract that The trained CNN required 7.0 s to analyze the test
dataset. However, this is not stated in the text.
The difference in time with the endoscopists should be stated.
Fifth, it is not clear in which comparison there is a statistical difference. The p-value should be
stated in the text of the table and results.
assistant