Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 1 Date: 29 Jul 2024
Reviewer's report:
This article explores the construction of a prognostic model for pancreatic ductal
adenocarcinoma (PDAC) based on deep learning and digital pathology images, and improves
the accuracy of survival prognosis prediction by combining pathological risk models and
clinical risk models. The article still has some shortcomings and needs further modification
and improvement. Specific doubts: Data volume and sample representativeness: The data set
of the article comes from a single center, and the sample size is relatively small (142 cases).
The lack of multi-center and larger sample size verification may affect the universality and
credibility of the results. Lack of external validation: No external data set was verified in the
study, which makes it impossible to fully verify the generalization ability and stability of the
model. It is recommended that the author add an external validation set for evaluation in further
research. Model interpretability issues: The article mentions that the specific meaning of the
features extracted by the deep learning model is unclear, which may become an obstacle in
clinical application. It is recommended that the author increase the explanation and visualization
of important features in the model to improve the transparency and clinical acceptability of
the model. Image processing and feature extraction methods: Whether the selection of image
preprocessing and feature extraction methods is optimal is worth discussing. Although the
three pre-trained models (MobileNet_v3_small, ResNet18, and DenseNet121) used in the article
performed well, no comparative study was conducted on other potentially better models. It
is recommended to further explore and compare different deep learning models and feature
extraction methods. Unclear details of statistical methods: The description of the parameter
selection and validation process of the LASSO regression and Cox regression models in the
article is relatively simple. It is recommended to add detailed statistical method descriptions,
including specific criteria for parameter selection and details of cross-validation methods.
Selection and validation of biomarkers: In the selection process of clinical risk features, are
there any potentially important biomarkers that have not been considered? It is recommended to
screen and validate more biomarkers to improve the predictive ability of the model. Discussion:
The discussion of existing research is relatively brief and fails to fully reflect the innovation and
contribution of this study. It is recommended to increase the review and comparison of the latest
research in related fields to highlight the innovation and advantages of this article.
Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 1 Date: 29 Jul 2024
Reviewer's report:
This article explores the construction of a prognostic model for pancreatic ductal
adenocarcinoma (PDAC) based on deep learning and digital pathology images, and improves
the accuracy of survival prognosis prediction by combining pathological risk models and
clinical risk models. The article still has some shortcomings and needs further modification
and improvement. Specific doubts: Data volume and sample representativeness: The data set
of the article comes from a single center, and the sample size is relatively small (142 cases).
The lack of multi-center and larger sample size verification may affect the universality and
credibility of the results. Lack of external validation: No external data set was verified in the
study, which makes it impossible to fully verify the generalization ability and stability of the
model. It is recommended that the author add an external validation set for evaluation in further
research. Model interpretability issues: The article mentions that the specific meaning of the
features extracted by the deep learning model is unclear, which may become an obstacle in
clinical application. It is recommended that the author increase the explanation and visualization
of important features in the model to improve the transparency and clinical acceptability of
the model. Image processing and feature extraction methods: Whether the selection of image
preprocessing and feature extraction methods is optimal is worth discussing. Although the
three pre-trained models (MobileNet_v3_small, ResNet18, and DenseNet121) used in the article
performed well, no comparative study was conducted on other potentially better models. It
is recommended to further explore and compare different deep learning models and feature
extraction methods. Unclear details of statistical methods: The description of the parameter
selection and validation process of the LASSO regression and Cox regression models in the
article is relatively simple. It is recommended to add detailed statistical method descriptions,
including specific criteria for parameter selection and details of cross-validation methods.
Selection and validation of biomarkers: In the selection process of clinical risk features, are
there any potentially important biomarkers that have not been considered? It is recommended to
screen and validate more biomarkers to improve the predictive ability of the model. Discussion:
The discussion of existing research is relatively brief and fails to fully reflect the innovation and
contribution of this study. It is recommended to increase the review and comparison of the latest
research in related fields to highlight the innovation and advantages of this article.
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: 29 Jul 2024
Reviewer's report:
This article explores the construction of a prognostic model for pancreatic ductal
adenocarcinoma (PDAC) based on deep learning and digital pathology images, and improves
the accuracy of survival prognosis prediction by combining pathological risk models and
clinical risk models. The article still has some shortcomings and needs further modification
and improvement. Specific doubts: Data volume and sample representativeness: The data set
of the article comes from a single center, and the sample size is relatively small (142 cases).
The lack of multi-center and larger sample size verification may affect the universality and
credibility of the results. Lack of external validation: No external data set was verified in the
study, which makes it impossible to fully verify the generalization ability and stability of the
model. It is recommended that the author add an external validation set for evaluation in further
research. Model interpretability issues: The article mentions that the specific meaning of the
features extracted by the deep learning model is unclear, which may become an obstacle in
clinical application. It is recommended that the author increase the explanation and visualization
of important features in the model to improve the transparency and clinical acceptability of
the model. Image processing and feature extraction methods: Whether the selection of image
preprocessing and feature extraction methods is optimal is worth discussing. Although the
three pre-trained models (MobileNet_v3_small, ResNet18, and DenseNet121) used in the article
performed well, no comparative study was conducted on other potentially better models. It
is recommended to further explore and compare different deep learning models and feature
extraction methods. Unclear details of statistical methods: The description of the parameter
selection and validation process of the LASSO regression and Cox regression models in the
article is relatively simple. It is recommended to add detailed statistical method descriptions,
including specific criteria for parameter selection and details of cross-validation methods.
Selection and validation of biomarkers: In the selection process of clinical risk features, are
there any potentially important biomarkers that have not been considered? It is recommended to
screen and validate more biomarkers to improve the predictive ability of the model. Discussion:
The discussion of existing research is relatively brief and fails to fully reflect the innovation and
contribution of this study. It is recommended to increase the review and comparison of the latest
research in related fields to highlight the innovation and advantages of this article.
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