Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 4 Date: 12 Sep 2024
Reviewer's report:
It is a interesting study where authors use a multimodality deep learning model to predict the
prognosis of PDAC. But here are some questions.
1. When extracting the features of pathological images, how did the author deal with parts
that did not contain lesions? When a lesion divided into several patches, how to deal with this
situation?
2. The details of transfer learning should be included. For example, whether forzen the output
layers.
3. Compare to cox model, a machine learning can improve the performance of  multimodality
model.  Maybe you can refer to this research: The Development of a Prediction Model Based on
Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based
Study.
Title: Title#Construction of a Combined Prognostic Model for Pancreatic Ductal
Adenocarcinoma Based on Deep Learning and Digital Pathology Images
Version: 4 Date: 12 Sep 2024
Reviewer's report:
It is a interesting study where authors use a multimodality deep learning model to predict the
prognosis of PDAC. But here are some questions.
1. When extracting the features of pathological images, how did the author deal with parts
that did not contain lesions? When a lesion divided into several patches, how to deal with this
situation?
2. The details of transfer learning should be included. For example, whether forzen the output
layers.
3. Compare to cox model, a machine learning can improve the performance of  multimodality
model.  Maybe you can refer to this research: The Development of a Prediction Model Based on
Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based
Study.
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: 4 Date: 12 Sep 2024
Reviewer's report:
It is a interesting study where authors use a multimodality deep learning model to predict the
prognosis of PDAC. But here are some questions.
1. When extracting the features of pathological images, how did the author deal with parts
that did not contain lesions? When a lesion divided into several patches, how to deal with this
situation?
2. The details of transfer learning should be included. For example, whether forzen the output
layers.
3. Compare to cox model, a machine learning can improve the performance of  multimodality
model.  Maybe you can refer to this research: The Development of a Prediction Model Based on
Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based
Study.
assistant