Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 08 Feb 2023
Reviewer:  Kuan-chih Chen
Reviewer's report:
In this study, the authors demonstrated better performance for predicting cholangiocarcinoma
(CCA) in primary sclerosing cholangitis patients through artificial intelligence combined with
clinical and laboratory data. Although the manuscript was well-constructed and informative.
There are still several questions.
1. In your data set, can you describe more about different types of CCA (such as the location of
CCA) if available? Do you think that will affect your result?
2. Do you think that image data may be helpful for the prediction of CCA in PSC patients? Can
you have some discussion in the discussion part to explore its possible role?
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 08 Feb 2023
Reviewer:  Kuan-chih Chen
Reviewer's report:
In this study, the authors demonstrated better performance for predicting cholangiocarcinoma
(CCA) in primary sclerosing cholangitis patients through artificial intelligence combined with
clinical and laboratory data. Although the manuscript was well-constructed and informative.
There are still several questions.
1. In your data set, can you describe more about different types of CCA (such as the location of
CCA) if available? Do you think that will affect your result?
2. Do you think that image data may be helpful for the prediction of CCA in PSC patients? Can
you have some discussion in the discussion part to explore its possible role?
Please review this paper.
user
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 08 Feb 2023
Reviewer:  Kuan-chih Chen
Reviewer's report:
In this study, the authors demonstrated better performance for predicting cholangiocarcinoma
(CCA) in primary sclerosing cholangitis patients through artificial intelligence combined with
clinical and laboratory data. Although the manuscript was well-constructed and informative.
There are still several questions.
1. In your data set, can you describe more about different types of CCA (such as the location of
CCA) if available? Do you think that will affect your result?
2. Do you think that image data may be helpful for the prediction of CCA in PSC patients? Can
you have some discussion in the discussion part to explore its possible role?
assistant