Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 1 Date: 30 Jul 2024
Reviewer:  Zongren Ding
Reviewer's report:
The author utilizes clinical data from PDAC patients and whole slide images (WSI), employing
logistic regression and machine learning algorithms to construct a model for stratifying PDAC
patients, which holds certain clinical guidance significance.The article is innovative and easy
to read and understand. However, I would like the authors to address my queries noted below
to strengthen the work.
1.This is a single-center study conducted in China. Please provide an introduction that discusses
the epidemiological status of PDAC in China or Asia, rather than focusing on the situation in
the United States.
2.Please provide information on all variables prior to the Lasso regression. For PDAC, I am
particularly interested in indicators such as CA19-9, CEA, bilirubin, GGT, ALP, and tumor size.
3.The combined model significantly outperformed both the single PRS model and the single
CRS model in fitting survival data, with a likelihood ratio test result of P < 0.001. Please include
graphical representations, such as ROC curve comparisons, to illustrate this finding.
4.The clinical model constructed by the authors has a C-index of 0.75 in the validation group,
while the best deep learning model only achieved 0.73. Please explain or discuss the rationale for
using a deep learning model, as implementing deep learning is known to be a challenging task.
Overall, I suggest making the necessary modifications to address the points mentioned above.
Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 1 Date: 30 Jul 2024
Reviewer:  Zongren Ding
Reviewer's report:
The author utilizes clinical data from PDAC patients and whole slide images (WSI), employing
logistic regression and machine learning algorithms to construct a model for stratifying PDAC
patients, which holds certain clinical guidance significance.The article is innovative and easy
to read and understand. However, I would like the authors to address my queries noted below
to strengthen the work.
1.This is a single-center study conducted in China. Please provide an introduction that discusses
the epidemiological status of PDAC in China or Asia, rather than focusing on the situation in
the United States.
2.Please provide information on all variables prior to the Lasso regression. For PDAC, I am
particularly interested in indicators such as CA19-9, CEA, bilirubin, GGT, ALP, and tumor size.
3.The combined model significantly outperformed both the single PRS model and the single
CRS model in fitting survival data, with a likelihood ratio test result of P < 0.001. Please include
graphical representations, such as ROC curve comparisons, to illustrate this finding.
4.The clinical model constructed by the authors has a C-index of 0.75 in the validation group,
while the best deep learning model only achieved 0.73. Please explain or discuss the rationale for
using a deep learning model, as implementing deep learning is known to be a challenging task.
Overall, I suggest making the necessary modifications to address the points mentioned above.
Please review this paper.
user
Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 1 Date: 30 Jul 2024
Reviewer:  Zongren Ding
Reviewer's report:
The author utilizes clinical data from PDAC patients and whole slide images (WSI), employing
logistic regression and machine learning algorithms to construct a model for stratifying PDAC
patients, which holds certain clinical guidance significance.The article is innovative and easy
to read and understand. However, I would like the authors to address my queries noted below
to strengthen the work.
1.This is a single-center study conducted in China. Please provide an introduction that discusses
the epidemiological status of PDAC in China or Asia, rather than focusing on the situation in
the United States.
2.Please provide information on all variables prior to the Lasso regression. For PDAC, I am
particularly interested in indicators such as CA19-9, CEA, bilirubin, GGT, ALP, and tumor size.
3.The combined model significantly outperformed both the single PRS model and the single
CRS model in fitting survival data, with a likelihood ratio test result of P < 0.001. Please include
graphical representations, such as ROC curve comparisons, to illustrate this finding.
4.The clinical model constructed by the authors has a C-index of 0.75 in the validation group,
while the best deep learning model only achieved 0.73. Please explain or discuss the rationale for
using a deep learning model, as implementing deep learning is known to be a challenging task.
Overall, I suggest making the necessary modifications to address the points mentioned above.
assistant