Title: Diagnosis model for gastric submucosal tumor based on multiple decision trees
comprising endoscopic and endoscopic ultrasonography features
Version: 0 Date: 28 Oct 2024
Reviewer's report:
The study by Shen Su et al. presents a hierarchical decision tree model for the preoperative
diagnosis of some gastric subepithelial lesions (SELs) using basic endoscopic ultrasound (EUS)
features. The authors aim to improve the differentiation of SEL subtypes, which is crucial for
guiding follow up and treatment decisions.
The abstract provides a concise overview of the study. However, the retrospective nature of the
study should be specified in the methods section.
The introduction sets the stage by explaining the clinical importance of accurately diagnosing
SELs, the limitations of current diagnostic methods like EUS-FNA, and the potential benefits of
a non-invasive diagnostic model. However, authors should consider enriching the introduction
with existing non-invasive diagnostic methods such as contrast enhanced EUS and AI-supported
diagnosis in order to provide a better context for the current study.
In the methods section clear inclusion and exclusion criteria are provided and the decision to
use a hierarchical model of multiple decision trees is well-justified, considering the limitations
of a single multi-classification tree. Statistical methods for variable selection are appropriately
detailed (chi-square test, independence test, Pearson correlation) and the model evaluation
metrics are defined (AUC, precision, recall, F1), and their relevance is explained.
The results are well presented but some figures present errors. In particular: Fig. 5 represent
the decision tree of IFP and not of lipoma as stated; Fig. 6 represent figure tree of lipomas and
not IFP. Moreover, in this decision tree if the lesion does NOT originate from the mucolaris
propria it is suggestive for lipomas and should be marked as such in red. On the other hand,
if the lesion originates from muscolaris propria it is not suggestive for lipomas and should be
marked in grey. Please consider reporting numbers of individuals at each stage of study—eg
numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study
– and complete with a flow diagram.
In the discussion section authors highlight the novelty of using a hierarchical decision
tree model for SEL differentiation, but there is limited comparison with AI models
(authors mention the work of Hirai at al but others papers should be discussed such as
https://pubmed.ncbi.nlm.nih.gov/36100781/ and https://pubmed.ncbi.nlm.nih.gov/32918102/)
or others diagnostic models such as contrast enhancing EUS. Please revise.
Given the limitations of the study, well recognized in the discussion section, the conclusions
are partially coherent with the results but may overstate the model's readiness for clinical
application. A more cautious interpretation that emphasizes the need for further validation and
refinement would be appropriate. In particular, an overall accuracy of 75.12% is not “great” and,
without further studies and external validation, the model is not ready for be used as an effective
tool for individualized diagnosis and treatment for SEL.
Title: Diagnosis model for gastric submucosal tumor based on multiple decision trees
comprising endoscopic and endoscopic ultrasonography features
Version: 0 Date: 28 Oct 2024
Reviewer's report:
The study by Shen Su et al. presents a hierarchical decision tree model for the preoperative
diagnosis of some gastric subepithelial lesions (SELs) using basic endoscopic ultrasound (EUS)
features. The authors aim to improve the differentiation of SEL subtypes, which is crucial for
guiding follow up and treatment decisions.
The abstract provides a concise overview of the study. However, the retrospective nature of the
study should be specified in the methods section.
The introduction sets the stage by explaining the clinical importance of accurately diagnosing
SELs, the limitations of current diagnostic methods like EUS-FNA, and the potential benefits of
a non-invasive diagnostic model. However, authors should consider enriching the introduction
with existing non-invasive diagnostic methods such as contrast enhanced EUS and AI-supported
diagnosis in order to provide a better context for the current study.
In the methods section clear inclusion and exclusion criteria are provided and the decision to
use a hierarchical model of multiple decision trees is well-justified, considering the limitations
of a single multi-classification tree. Statistical methods for variable selection are appropriately
detailed (chi-square test, independence test, Pearson correlation) and the model evaluation
metrics are defined (AUC, precision, recall, F1), and their relevance is explained.
The results are well presented but some figures present errors. In particular: Fig. 5 represent
the decision tree of IFP and not of lipoma as stated; Fig. 6 represent figure tree of lipomas and
not IFP. Moreover, in this decision tree if the lesion does NOT originate from the mucolaris
propria it is suggestive for lipomas and should be marked as such in red. On the other hand,
if the lesion originates from muscolaris propria it is not suggestive for lipomas and should be
marked in grey. Please consider reporting numbers of individuals at each stage of study—eg
numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study
– and complete with a flow diagram.
In the discussion section authors highlight the novelty of using a hierarchical decision
tree model for SEL differentiation, but there is limited comparison with AI models
(authors mention the work of Hirai at al but others papers should be discussed such as
https://pubmed.ncbi.nlm.nih.gov/36100781/ and https://pubmed.ncbi.nlm.nih.gov/32918102/)
or others diagnostic models such as contrast enhancing EUS. Please revise.
Given the limitations of the study, well recognized in the discussion section, the conclusions
are partially coherent with the results but may overstate the model's readiness for clinical
application. A more cautious interpretation that emphasizes the need for further validation and
refinement would be appropriate. In particular, an overall accuracy of 75.12% is not “great” and,
without further studies and external validation, the model is not ready for be used as an effective
tool for individualized diagnosis and treatment for SEL.
Please review this paper.
user
Title: Diagnosis model for gastric submucosal tumor based on multiple decision trees
comprising endoscopic and endoscopic ultrasonography features
Version: 0 Date: 28 Oct 2024
Reviewer's report:
The study by Shen Su et al. presents a hierarchical decision tree model for the preoperative
diagnosis of some gastric subepithelial lesions (SELs) using basic endoscopic ultrasound (EUS)
features. The authors aim to improve the differentiation of SEL subtypes, which is crucial for
guiding follow up and treatment decisions.
The abstract provides a concise overview of the study. However, the retrospective nature of the
study should be specified in the methods section.
The introduction sets the stage by explaining the clinical importance of accurately diagnosing
SELs, the limitations of current diagnostic methods like EUS-FNA, and the potential benefits of
a non-invasive diagnostic model. However, authors should consider enriching the introduction
with existing non-invasive diagnostic methods such as contrast enhanced EUS and AI-supported
diagnosis in order to provide a better context for the current study.
In the methods section clear inclusion and exclusion criteria are provided and the decision to
use a hierarchical model of multiple decision trees is well-justified, considering the limitations
of a single multi-classification tree. Statistical methods for variable selection are appropriately
detailed (chi-square test, independence test, Pearson correlation) and the model evaluation
metrics are defined (AUC, precision, recall, F1), and their relevance is explained.
The results are well presented but some figures present errors. In particular: Fig. 5 represent
the decision tree of IFP and not of lipoma as stated; Fig. 6 represent figure tree of lipomas and
not IFP. Moreover, in this decision tree if the lesion does NOT originate from the mucolaris
propria it is suggestive for lipomas and should be marked as such in red. On the other hand,
if the lesion originates from muscolaris propria it is not suggestive for lipomas and should be
marked in grey. Please consider reporting numbers of individuals at each stage of study—eg
numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study
– and complete with a flow diagram.
In the discussion section authors highlight the novelty of using a hierarchical decision
tree model for SEL differentiation, but there is limited comparison with AI models
(authors mention the work of Hirai at al but others papers should be discussed such as
https://pubmed.ncbi.nlm.nih.gov/36100781/ and https://pubmed.ncbi.nlm.nih.gov/32918102/)
or others diagnostic models such as contrast enhancing EUS. Please revise.
Given the limitations of the study, well recognized in the discussion section, the conclusions
are partially coherent with the results but may overstate the model's readiness for clinical
application. A more cautious interpretation that emphasizes the need for further validation and
refinement would be appropriate. In particular, an overall accuracy of 75.12% is not “great” and,
without further studies and external validation, the model is not ready for be used as an effective
tool for individualized diagnosis and treatment for SEL.
assistant