Title: Ultrasound Radiomics-Based Logistic Regression Model for Fibrotic NASH
Version: 3 Date: 12 Dec 2024
Reviewer's report:
Thank you for your efforts in evaluating the crucial histopathological index, fibrosis, based on
artificial intelligence and radiomics features. The base of the manuscript is well structured, but it
consists of some grammatical errors and lacks in the structure that must be considered. Based on
the overall content of the manuscript, the following modifications can be considered to improve
the clarity of the effort.
1- The literature review is so weak, and it doesn’t provide a clear view of the previous works
on the subject of the machine learning approaches in fibrotic NASH and fibrosis pathological
index. Besides, the performance of the previous studies was not presented to compare
2- The novelty of the study is not clear and according to the introduction and the procedure of
the study, it is so weak to be reported in this journal.
3- The standard guideline of the preparation of the rat samples in their diets was not mentioned.
4- What is the reason of choosing the blood biomarkers? Describe it in detail.
5- The lack of the citation to the claims in the manuscript is obvious. For example, there is no
evidence for definition of “fibrotic NASH”. Review carefully the manuscript and modify it based
on the certified references. The restriction of the citation degrades crucially the importance of
the study.
6- The hyperparameters of configuring the approaches in radiomics algorithm must be described,
such as the step of the neighborhood, the angle of survey, the overall characteristics of each
approach, the overall number of the extracted features by radiomics, etc.
7- Describe in detail the LASSO technique, Z-score, nomogram, and rad-score to clarify the
mechanism of feature selection, along with their references.
8- The misplacement of the dataset description in section 3-1 confuses the reader. The Results
section must describe the findings, solely and any discussion should be place in the related part.
9-  The overall radiomics features by “pyradiomics” package are limited. What is your
mechanism for extracting 1288 texture features and what was your mechanism to overcome the
crucial of overfitting and keeping the generalization of the model? Describe in detail.
10- What is the mechanism of choosing LASSO technique for feature selection and what is the
performance by employing alternative approaches?
11- The borderline of fibrosis is in stage 1 and 2. What is your mechanism to distinguish between
then and choosing F2, as critical point of fibrotic NASH?
12- What is your guideline or expert team to define the gold standard of fibrosis index? Describe
in detail
13- The application of logistic regression model does not guarantee the appropriate performance
for final model. A comparison of the performance of this algorithm should be presented with
other applicable methods, such as Random Forest, LightGBM, SVM, XGBoost, AdaBoost, etc.
Title: Ultrasound Radiomics-Based Logistic Regression Model for Fibrotic NASH
Version: 3 Date: 12 Dec 2024
Reviewer's report:
Thank you for your efforts in evaluating the crucial histopathological index, fibrosis, based on
artificial intelligence and radiomics features. The base of the manuscript is well structured, but it
consists of some grammatical errors and lacks in the structure that must be considered. Based on
the overall content of the manuscript, the following modifications can be considered to improve
the clarity of the effort.
1- The literature review is so weak, and it doesn’t provide a clear view of the previous works
on the subject of the machine learning approaches in fibrotic NASH and fibrosis pathological
index. Besides, the performance of the previous studies was not presented to compare
2- The novelty of the study is not clear and according to the introduction and the procedure of
the study, it is so weak to be reported in this journal.
3- The standard guideline of the preparation of the rat samples in their diets was not mentioned.
4- What is the reason of choosing the blood biomarkers? Describe it in detail.
5- The lack of the citation to the claims in the manuscript is obvious. For example, there is no
evidence for definition of “fibrotic NASH”. Review carefully the manuscript and modify it based
on the certified references. The restriction of the citation degrades crucially the importance of
the study.
6- The hyperparameters of configuring the approaches in radiomics algorithm must be described,
such as the step of the neighborhood, the angle of survey, the overall characteristics of each
approach, the overall number of the extracted features by radiomics, etc.
7- Describe in detail the LASSO technique, Z-score, nomogram, and rad-score to clarify the
mechanism of feature selection, along with their references.
8- The misplacement of the dataset description in section 3-1 confuses the reader. The Results
section must describe the findings, solely and any discussion should be place in the related part.
9-  The overall radiomics features by “pyradiomics” package are limited. What is your
mechanism for extracting 1288 texture features and what was your mechanism to overcome the
crucial of overfitting and keeping the generalization of the model? Describe in detail.
10- What is the mechanism of choosing LASSO technique for feature selection and what is the
performance by employing alternative approaches?
11- The borderline of fibrosis is in stage 1 and 2. What is your mechanism to distinguish between
then and choosing F2, as critical point of fibrotic NASH?
12- What is your guideline or expert team to define the gold standard of fibrosis index? Describe
in detail
13- The application of logistic regression model does not guarantee the appropriate performance
for final model. A comparison of the performance of this algorithm should be presented with
other applicable methods, such as Random Forest, LightGBM, SVM, XGBoost, AdaBoost, etc.
Please review this paper.
user
Title: Ultrasound Radiomics-Based Logistic Regression Model for Fibrotic NASH
Version: 3 Date: 12 Dec 2024
Reviewer's report:
Thank you for your efforts in evaluating the crucial histopathological index, fibrosis, based on
artificial intelligence and radiomics features. The base of the manuscript is well structured, but it
consists of some grammatical errors and lacks in the structure that must be considered. Based on
the overall content of the manuscript, the following modifications can be considered to improve
the clarity of the effort.
1- The literature review is so weak, and it doesn’t provide a clear view of the previous works
on the subject of the machine learning approaches in fibrotic NASH and fibrosis pathological
index. Besides, the performance of the previous studies was not presented to compare
2- The novelty of the study is not clear and according to the introduction and the procedure of
the study, it is so weak to be reported in this journal.
3- The standard guideline of the preparation of the rat samples in their diets was not mentioned.
4- What is the reason of choosing the blood biomarkers? Describe it in detail.
5- The lack of the citation to the claims in the manuscript is obvious. For example, there is no
evidence for definition of “fibrotic NASH”. Review carefully the manuscript and modify it based
on the certified references. The restriction of the citation degrades crucially the importance of
the study.
6- The hyperparameters of configuring the approaches in radiomics algorithm must be described,
such as the step of the neighborhood, the angle of survey, the overall characteristics of each
approach, the overall number of the extracted features by radiomics, etc.
7- Describe in detail the LASSO technique, Z-score, nomogram, and rad-score to clarify the
mechanism of feature selection, along with their references.
8- The misplacement of the dataset description in section 3-1 confuses the reader. The Results
section must describe the findings, solely and any discussion should be place in the related part.
9-  The overall radiomics features by “pyradiomics” package are limited. What is your
mechanism for extracting 1288 texture features and what was your mechanism to overcome the
crucial of overfitting and keeping the generalization of the model? Describe in detail.
10- What is the mechanism of choosing LASSO technique for feature selection and what is the
performance by employing alternative approaches?
11- The borderline of fibrosis is in stage 1 and 2. What is your mechanism to distinguish between
then and choosing F2, as critical point of fibrotic NASH?
12- What is your guideline or expert team to define the gold standard of fibrosis index? Describe
in detail
13- The application of logistic regression model does not guarantee the appropriate performance
for final model. A comparison of the performance of this algorithm should be presented with
other applicable methods, such as Random Forest, LightGBM, SVM, XGBoost, AdaBoost, etc.
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