Title: Diagnosis model for gastric submucosal tumor based on multiple decision trees
comprising endoscopic and endoscopic ultrasonography features
Version: 0 Date: 14 Oct 2024
Reviewer's report:
Dear authors,
I read with great interest your paper.
Regarding your manuscript, I believe the paper is well-written and clear in its aims. However,
it could benefit from revisions in both language and structure, and some typos need correction.
In particular, the Materials and Methods section requires a significant revision, as it is currently
difficult to understand and would benefit from simplification. There are some errors that should
be addressed (at least basing on my understanding of the work). Eg. on page 5, lines 95-98
are not clear to me. Additionally, on page 5, line 106, considering the phrase “If the latter, the
decision tree 3.4.5 determines” if I’m not wrong the decision tree 3 should not be included.
Furthermore, Decision Tree 2 is not included in the final algorithm (Figure 1c). I also suggest a
careful review of Figure 1c considering some mistakes I found.
Regarding the diagnostic model itself, I believe it is important to acknowledge the role of FNB
in the assessment of SELs that may require resection. FNB is not included in the algorithm. This
might explain why some benign lesions, such as lipomas and ectopic pancreas, were resected
in the study.
When considering the application of this work to clinical practice, it appears quite complex and
time-consuming. Evaluating 13 variables, applying separate decision trees for each pathological
type, and then completing the entire algorithm seems cumbersome. There are too many decision
points at each step. If Figure 1c represents the final algorithm to be used in clinical settings,
the number of questions that must be answered to achieve 75% overall accuracy is quite high.
I would suggest reducing the number or unifying some variables to make the process more
practical.
Another point to consider is that only six types of gastric SELs were included in the study. For
example, NETs were not part of the cohort. Additionally, since the patient cohort consists only
of those who underwent endoscopic resection, size should not be included in the algorithm due
to selection bias. Smaller lesions are often kept under surveillance, while larger ones may be
referred directly for surgery rather than endoscopic resection.
Title: Diagnosis model for gastric submucosal tumor based on multiple decision trees
comprising endoscopic and endoscopic ultrasonography features
Version: 0 Date: 14 Oct 2024
Reviewer's report:
Dear authors,
I read with great interest your paper.
Regarding your manuscript, I believe the paper is well-written and clear in its aims. However,
it could benefit from revisions in both language and structure, and some typos need correction.
In particular, the Materials and Methods section requires a significant revision, as it is currently
difficult to understand and would benefit from simplification. There are some errors that should
be addressed (at least basing on my understanding of the work). Eg. on page 5, lines 95-98
are not clear to me. Additionally, on page 5, line 106, considering the phrase “If the latter, the
decision tree 3.4.5 determines” if I’m not wrong the decision tree 3 should not be included.
Furthermore, Decision Tree 2 is not included in the final algorithm (Figure 1c). I also suggest a
careful review of Figure 1c considering some mistakes I found.
Regarding the diagnostic model itself, I believe it is important to acknowledge the role of FNB
in the assessment of SELs that may require resection. FNB is not included in the algorithm. This
might explain why some benign lesions, such as lipomas and ectopic pancreas, were resected
in the study.
When considering the application of this work to clinical practice, it appears quite complex and
time-consuming. Evaluating 13 variables, applying separate decision trees for each pathological
type, and then completing the entire algorithm seems cumbersome. There are too many decision
points at each step. If Figure 1c represents the final algorithm to be used in clinical settings,
the number of questions that must be answered to achieve 75% overall accuracy is quite high.
I would suggest reducing the number or unifying some variables to make the process more
practical.
Another point to consider is that only six types of gastric SELs were included in the study. For
example, NETs were not part of the cohort. Additionally, since the patient cohort consists only
of those who underwent endoscopic resection, size should not be included in the algorithm due
to selection bias. Smaller lesions are often kept under surveillance, while larger ones may be
referred directly for surgery rather than endoscopic resection.
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: 14 Oct 2024
Reviewer's report:
Dear authors,
I read with great interest your paper.
Regarding your manuscript, I believe the paper is well-written and clear in its aims. However,
it could benefit from revisions in both language and structure, and some typos need correction.
In particular, the Materials and Methods section requires a significant revision, as it is currently
difficult to understand and would benefit from simplification. There are some errors that should
be addressed (at least basing on my understanding of the work). Eg. on page 5, lines 95-98
are not clear to me. Additionally, on page 5, line 106, considering the phrase “If the latter, the
decision tree 3.4.5 determines” if I’m not wrong the decision tree 3 should not be included.
Furthermore, Decision Tree 2 is not included in the final algorithm (Figure 1c). I also suggest a
careful review of Figure 1c considering some mistakes I found.
Regarding the diagnostic model itself, I believe it is important to acknowledge the role of FNB
in the assessment of SELs that may require resection. FNB is not included in the algorithm. This
might explain why some benign lesions, such as lipomas and ectopic pancreas, were resected
in the study.
When considering the application of this work to clinical practice, it appears quite complex and
time-consuming. Evaluating 13 variables, applying separate decision trees for each pathological
type, and then completing the entire algorithm seems cumbersome. There are too many decision
points at each step. If Figure 1c represents the final algorithm to be used in clinical settings,
the number of questions that must be answered to achieve 75% overall accuracy is quite high.
I would suggest reducing the number or unifying some variables to make the process more
practical.
Another point to consider is that only six types of gastric SELs were included in the study. For
example, NETs were not part of the cohort. Additionally, since the patient cohort consists only
of those who underwent endoscopic resection, size should not be included in the algorithm due
to selection bias. Smaller lesions are often kept under surveillance, while larger ones may be
referred directly for surgery rather than endoscopic resection.
assistant