Title: Development and Validation of a Novel Diagnostic Model for Initially Clinical Diagnosed
Gastrointestinal Stromal Tumors using an Extreme Gradient-Boosting Machine
Version: 1 Date: 20 Oct 2021
Reviewer:  Bo Zhang
Reviewer's report:
This is an interesting and well planned, performed, analyzed and reported review. The sample
size is large considering the rarity of the conditions and conclusions are well funded, based on the
results. The authors developed a novel diagnostic model to further differential diagnose GIST
from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results
of clinical examinations that most patients should have completed before surgical resection.
The accuracy of model prediction in the validation dataset is 0.73 (0.58-0.88), precision is
0.79 (0.60-0.95), recall is 0.87 (0.67-1.00), and f1-score is 0.82 (0.70-0.92). These results
were not significantly superior to previous studies in which traditional CT features were used.
Furthermore, only two   patients were selected to test the prediction quality of the model at the
Individual Scale, and more patients should be included.
Title: Development and Validation of a Novel Diagnostic Model for Initially Clinical Diagnosed
Gastrointestinal Stromal Tumors using an Extreme Gradient-Boosting Machine
Version: 1 Date: 20 Oct 2021
Reviewer:  Bo Zhang
Reviewer's report:
This is an interesting and well planned, performed, analyzed and reported review. The sample
size is large considering the rarity of the conditions and conclusions are well funded, based on the
results. The authors developed a novel diagnostic model to further differential diagnose GIST
from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results
of clinical examinations that most patients should have completed before surgical resection.
The accuracy of model prediction in the validation dataset is 0.73 (0.58-0.88), precision is
0.79 (0.60-0.95), recall is 0.87 (0.67-1.00), and f1-score is 0.82 (0.70-0.92). These results
were not significantly superior to previous studies in which traditional CT features were used.
Furthermore, only two   patients were selected to test the prediction quality of the model at the
Individual Scale, and more patients should be included.
Please review this paper.
user
Title: Development and Validation of a Novel Diagnostic Model for Initially Clinical Diagnosed
Gastrointestinal Stromal Tumors using an Extreme Gradient-Boosting Machine
Version: 1 Date: 20 Oct 2021
Reviewer:  Bo Zhang
Reviewer's report:
This is an interesting and well planned, performed, analyzed and reported review. The sample
size is large considering the rarity of the conditions and conclusions are well funded, based on the
results. The authors developed a novel diagnostic model to further differential diagnose GIST
from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results
of clinical examinations that most patients should have completed before surgical resection.
The accuracy of model prediction in the validation dataset is 0.73 (0.58-0.88), precision is
0.79 (0.60-0.95), recall is 0.87 (0.67-1.00), and f1-score is 0.82 (0.70-0.92). These results
were not significantly superior to previous studies in which traditional CT features were used.
Furthermore, only two   patients were selected to test the prediction quality of the model at the
Individual Scale, and more patients should be included.
assistant