Title: An artificial intelligence model utilizing endoscopic ultrasonography for differentiating
small and micro gastric stromal tumors from gastric leiomyomas
Version: 0 Date: 13 Dec 2024
Reviewer's report:
This study created an AI model for differentiating small GST from GL. The idea is unique and
interesting, because characterizing small gastric subepithelial lesions is a challeng. However, I
found several concerns on it.
Major
#In this study, 67 patients with a GST and 75 with a GLS were enrolled. Referring to your
inclusion criteria (a), the final diagnoses were based on surgery or endoscopic resection. Please
explain whetherthe rate of GL resected by surgery is comparable to that of GST.  If those values
significantly differ, it should be a bias.
#What was the diagnostic accuracy of endoscopic ultrasonography guided tissue acquisition
(EUS-TA) for the SMTs of 20 mm or less in diameter?  Comparing diagnostic accuracy of AI
imaging and EUS-TA may strengthen role of AI imaging.
#Please make a paragraph explaining about details of all CNN models (ResNet50, DensNet201,
and VGG19).
#You evaluated LD/SD ratio. Please describe how the tumor diameter was measured. Did you
measure LD and SD on the same image; how was the EUS image selected?
#Are there any significant differences in the AUROC between the six tests in Table 1 and Figure
2, namely LR, SVM, RF, XGBoost, LightGBM and ExtraTrees?
#(Page12 Line 278) Authors mentioned that ResNet50 surpassed the other models by
demonstrating consistently high AUC scores across various cohorts. However, No apparent
difference in AUROC can been seen. Are there significant differences?
Minor
#(Page 6, Line126 and Supplementary file 1)
Figure 1 and Supplementary table 1 show the details about enrolled patients. However, the
number of enrolled patients were different between the text, and Figure 1 and Supplementary
table 1. Please check the number of enrolled patients.
#(Page 15, Line 298)
Authors need to explain what the tumor-centric approach means? What did EUS-AI focus on
for tumor-centric approach? EUS-AI focused on echoic level?
#This study focuses on GST and GL in the stomach, but SMT also includes schwannomas and
aberrant pancreas in ordinary clinical practices. Why were they not included in this study?
Title: An artificial intelligence model utilizing endoscopic ultrasonography for differentiating
small and micro gastric stromal tumors from gastric leiomyomas
Version: 0 Date: 13 Dec 2024
Reviewer's report:
This study created an AI model for differentiating small GST from GL. The idea is unique and
interesting, because characterizing small gastric subepithelial lesions is a challeng. However, I
found several concerns on it.
Major
#In this study, 67 patients with a GST and 75 with a GLS were enrolled. Referring to your
inclusion criteria (a), the final diagnoses were based on surgery or endoscopic resection. Please
explain whetherthe rate of GL resected by surgery is comparable to that of GST.  If those values
significantly differ, it should be a bias.
#What was the diagnostic accuracy of endoscopic ultrasonography guided tissue acquisition
(EUS-TA) for the SMTs of 20 mm or less in diameter?  Comparing diagnostic accuracy of AI
imaging and EUS-TA may strengthen role of AI imaging.
#Please make a paragraph explaining about details of all CNN models (ResNet50, DensNet201,
and VGG19).
#You evaluated LD/SD ratio. Please describe how the tumor diameter was measured. Did you
measure LD and SD on the same image; how was the EUS image selected?
#Are there any significant differences in the AUROC between the six tests in Table 1 and Figure
2, namely LR, SVM, RF, XGBoost, LightGBM and ExtraTrees?
#(Page12 Line 278) Authors mentioned that ResNet50 surpassed the other models by
demonstrating consistently high AUC scores across various cohorts. However, No apparent
difference in AUROC can been seen. Are there significant differences?
Minor
#(Page 6, Line126 and Supplementary file 1)
Figure 1 and Supplementary table 1 show the details about enrolled patients. However, the
number of enrolled patients were different between the text, and Figure 1 and Supplementary
table 1. Please check the number of enrolled patients.
#(Page 15, Line 298)
Authors need to explain what the tumor-centric approach means? What did EUS-AI focus on
for tumor-centric approach? EUS-AI focused on echoic level?
#This study focuses on GST and GL in the stomach, but SMT also includes schwannomas and
aberrant pancreas in ordinary clinical practices. Why were they not included in this study?
Please review this paper.
user
Title: An artificial intelligence model utilizing endoscopic ultrasonography for differentiating
small and micro gastric stromal tumors from gastric leiomyomas
Version: 0 Date: 13 Dec 2024
Reviewer's report:
This study created an AI model for differentiating small GST from GL. The idea is unique and
interesting, because characterizing small gastric subepithelial lesions is a challeng. However, I
found several concerns on it.
Major
#In this study, 67 patients with a GST and 75 with a GLS were enrolled. Referring to your
inclusion criteria (a), the final diagnoses were based on surgery or endoscopic resection. Please
explain whetherthe rate of GL resected by surgery is comparable to that of GST.  If those values
significantly differ, it should be a bias.
#What was the diagnostic accuracy of endoscopic ultrasonography guided tissue acquisition
(EUS-TA) for the SMTs of 20 mm or less in diameter?  Comparing diagnostic accuracy of AI
imaging and EUS-TA may strengthen role of AI imaging.
#Please make a paragraph explaining about details of all CNN models (ResNet50, DensNet201,
and VGG19).
#You evaluated LD/SD ratio. Please describe how the tumor diameter was measured. Did you
measure LD and SD on the same image; how was the EUS image selected?
#Are there any significant differences in the AUROC between the six tests in Table 1 and Figure
2, namely LR, SVM, RF, XGBoost, LightGBM and ExtraTrees?
#(Page12 Line 278) Authors mentioned that ResNet50 surpassed the other models by
demonstrating consistently high AUC scores across various cohorts. However, No apparent
difference in AUROC can been seen. Are there significant differences?
Minor
#(Page 6, Line126 and Supplementary file 1)
Figure 1 and Supplementary table 1 show the details about enrolled patients. However, the
number of enrolled patients were different between the text, and Figure 1 and Supplementary
table 1. Please check the number of enrolled patients.
#(Page 15, Line 298)
Authors need to explain what the tumor-centric approach means? What did EUS-AI focus on
for tumor-centric approach? EUS-AI focused on echoic level?
#This study focuses on GST and GL in the stomach, but SMT also includes schwannomas and
aberrant pancreas in ordinary clinical practices. Why were they not included in this study?
assistant