Title: Development and verification of a radiomics model to forecast Ki67 index and prognosis
in advanced gastric tubular adenocarcinoma
Version: 0 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the LASSO method for feature selection is mentioned, the rationale behind selecting only
6 features needs further explanation. It would be beneficial to discuss why only 6 features were
chosen, particularly in the context of the 415 features initially retained after the ICC analysis.
Additionally, how were these 6 features identified in the final model as the most significant? A
more detailed explanation of the feature selection process could help readers better understand
the model's development.
-Radiomics models often involve high-dimensional data. A brief discussion on how the
dimensionality reduction methods (e.g., LASSO) addressed overfitting and potential loss of
important features would be helpful.
-The performance of the model in external validation (TCIA dataset) could be presented in
more detail. The AUC values for the training, validation 1, and validation 2 sets are reported,
but a deeper comparison of performance across these datasets, especially with regards to
generalizability, would strengthen the findings. It would be beneficial to include specific metrics
(e.g., sensitivity, specificity, accuracy) for the external validation set as well.
-While the AUC values indicate moderate model performance, further clarification on how
these values translate to clinical utility is warranted. For example, what level of AUC would
be considered clinically meaningful in predicting Ki67 index and prognosis in gastric cancer?
Providing benchmarks from similar studies or expert opinions would add context to these
findings.
-The authors mention that the model showed "moderate accuracy" with AUC values of 0.634,
0.666, and 0.602 across the training and validation sets. While these results are useful, the
discussion could benefit from a deeper exploration of the model's performance in real-world
clinical settings. For example, how do these AUC values compare with existing models or
methods for predicting Ki67 or prognosis in gastric cancer?
-The authors should also provide more context around the moderate AUC values. For instance,
what level of accuracy would be clinically meaningful in this context? Are these values sufficient
to inform clinical decision-making, or is further refinement required?
-The authors report an unexpected finding where higher Ki67 expression was associated with
a better prognosis in gastric cancer, which contradicts conventional expectations. While the
manuscript provides a potential explanation for this, including additional studies or references
to support this interpretation would be beneficial. For example, have other studies observed a
similar trend in advanced gastric tubular adenocarcinoma, or is this unique to this cohort?
-The authors suggest that high Ki67 expression could be associated with a better response to
certain treatments, such as chemotherapy or radiation. This hypothesis is interesting and could
be expanded upon. For example, could the authors elaborate on specific treatments or regimens
that patients with higher Ki67 expression might respond to better?
-The manuscript emphasizes that Ki67 is a critical marker for prognosis in various cancers.
However, more detailed discussion on how the Ki67 index directly influences treatment
decisions or patient outcomes in gastric cancer specifically would be helpful. For instance, how
can this model be used in clinical practice to stratify patients for personalized treatment plans
(e.g., chemotherapy regimens, surgical interventions)?
-Additionally, the authors mention that Ki67's prognostic value may depend on the tumor's
molecular and histological context. It would be useful to discuss how different molecular
subtypes of gastric cancer could potentially influence the predictive accuracy of the radiomics
model. Could the model be further refined to account for molecular heterogeneity in gastric
cancer?
Title: Development and verification of a radiomics model to forecast Ki67 index and prognosis
in advanced gastric tubular adenocarcinoma
Version: 0 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the LASSO method for feature selection is mentioned, the rationale behind selecting only
6 features needs further explanation. It would be beneficial to discuss why only 6 features were
chosen, particularly in the context of the 415 features initially retained after the ICC analysis.
Additionally, how were these 6 features identified in the final model as the most significant? A
more detailed explanation of the feature selection process could help readers better understand
the model's development.
-Radiomics models often involve high-dimensional data. A brief discussion on how the
dimensionality reduction methods (e.g., LASSO) addressed overfitting and potential loss of
important features would be helpful.
-The performance of the model in external validation (TCIA dataset) could be presented in
more detail. The AUC values for the training, validation 1, and validation 2 sets are reported,
but a deeper comparison of performance across these datasets, especially with regards to
generalizability, would strengthen the findings. It would be beneficial to include specific metrics
(e.g., sensitivity, specificity, accuracy) for the external validation set as well.
-While the AUC values indicate moderate model performance, further clarification on how
these values translate to clinical utility is warranted. For example, what level of AUC would
be considered clinically meaningful in predicting Ki67 index and prognosis in gastric cancer?
Providing benchmarks from similar studies or expert opinions would add context to these
findings.
-The authors mention that the model showed "moderate accuracy" with AUC values of 0.634,
0.666, and 0.602 across the training and validation sets. While these results are useful, the
discussion could benefit from a deeper exploration of the model's performance in real-world
clinical settings. For example, how do these AUC values compare with existing models or
methods for predicting Ki67 or prognosis in gastric cancer?
-The authors should also provide more context around the moderate AUC values. For instance,
what level of accuracy would be clinically meaningful in this context? Are these values sufficient
to inform clinical decision-making, or is further refinement required?
-The authors report an unexpected finding where higher Ki67 expression was associated with
a better prognosis in gastric cancer, which contradicts conventional expectations. While the
manuscript provides a potential explanation for this, including additional studies or references
to support this interpretation would be beneficial. For example, have other studies observed a
similar trend in advanced gastric tubular adenocarcinoma, or is this unique to this cohort?
-The authors suggest that high Ki67 expression could be associated with a better response to
certain treatments, such as chemotherapy or radiation. This hypothesis is interesting and could
be expanded upon. For example, could the authors elaborate on specific treatments or regimens
that patients with higher Ki67 expression might respond to better?
-The manuscript emphasizes that Ki67 is a critical marker for prognosis in various cancers.
However, more detailed discussion on how the Ki67 index directly influences treatment
decisions or patient outcomes in gastric cancer specifically would be helpful. For instance, how
can this model be used in clinical practice to stratify patients for personalized treatment plans
(e.g., chemotherapy regimens, surgical interventions)?
-Additionally, the authors mention that Ki67's prognostic value may depend on the tumor's
molecular and histological context. It would be useful to discuss how different molecular
subtypes of gastric cancer could potentially influence the predictive accuracy of the radiomics
model. Could the model be further refined to account for molecular heterogeneity in gastric
cancer?
Please review this paper.
user
Title: Development and verification of a radiomics model to forecast Ki67 index and prognosis
in advanced gastric tubular adenocarcinoma
Version: 0 Date: 15 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the LASSO method for feature selection is mentioned, the rationale behind selecting only
6 features needs further explanation. It would be beneficial to discuss why only 6 features were
chosen, particularly in the context of the 415 features initially retained after the ICC analysis.
Additionally, how were these 6 features identified in the final model as the most significant? A
more detailed explanation of the feature selection process could help readers better understand
the model's development.
-Radiomics models often involve high-dimensional data. A brief discussion on how the
dimensionality reduction methods (e.g., LASSO) addressed overfitting and potential loss of
important features would be helpful.
-The performance of the model in external validation (TCIA dataset) could be presented in
more detail. The AUC values for the training, validation 1, and validation 2 sets are reported,
but a deeper comparison of performance across these datasets, especially with regards to
generalizability, would strengthen the findings. It would be beneficial to include specific metrics
(e.g., sensitivity, specificity, accuracy) for the external validation set as well.
-While the AUC values indicate moderate model performance, further clarification on how
these values translate to clinical utility is warranted. For example, what level of AUC would
be considered clinically meaningful in predicting Ki67 index and prognosis in gastric cancer?
Providing benchmarks from similar studies or expert opinions would add context to these
findings.
-The authors mention that the model showed "moderate accuracy" with AUC values of 0.634,
0.666, and 0.602 across the training and validation sets. While these results are useful, the
discussion could benefit from a deeper exploration of the model's performance in real-world
clinical settings. For example, how do these AUC values compare with existing models or
methods for predicting Ki67 or prognosis in gastric cancer?
-The authors should also provide more context around the moderate AUC values. For instance,
what level of accuracy would be clinically meaningful in this context? Are these values sufficient
to inform clinical decision-making, or is further refinement required?
-The authors report an unexpected finding where higher Ki67 expression was associated with
a better prognosis in gastric cancer, which contradicts conventional expectations. While the
manuscript provides a potential explanation for this, including additional studies or references
to support this interpretation would be beneficial. For example, have other studies observed a
similar trend in advanced gastric tubular adenocarcinoma, or is this unique to this cohort?
-The authors suggest that high Ki67 expression could be associated with a better response to
certain treatments, such as chemotherapy or radiation. This hypothesis is interesting and could
be expanded upon. For example, could the authors elaborate on specific treatments or regimens
that patients with higher Ki67 expression might respond to better?
-The manuscript emphasizes that Ki67 is a critical marker for prognosis in various cancers.
However, more detailed discussion on how the Ki67 index directly influences treatment
decisions or patient outcomes in gastric cancer specifically would be helpful. For instance, how
can this model be used in clinical practice to stratify patients for personalized treatment plans
(e.g., chemotherapy regimens, surgical interventions)?
-Additionally, the authors mention that Ki67's prognostic value may depend on the tumor's
molecular and histological context. It would be useful to discuss how different molecular
subtypes of gastric cancer could potentially influence the predictive accuracy of the radiomics
model. Could the model be further refined to account for molecular heterogeneity in gastric
cancer?
assistant