Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 2 Date: 05 Dec 2022
Reviewer's report:
From line 112 to line 117, the defined diagnosis of cholangiocarcinoma includes cases without
histopathology or cytopathology. The authors included patients who would not be treated as
cholangiocarcinoma in clinical practice.
In the manuscript, the CA19-9 was used to diagnose and predict cholangiocarcinoma; the
reviewer thinks the process is not logically appropriate.
The study aimed at the prediction of cholangiocarcinoma among PSC patients. In line 126, the
author included medical records of healthy controls; the reviewer cannot find them in the flow
chart of the supplementation material. Have the health controls been analyzed?
Table_4 demonstrates the predictive model of the baseline cohort; the reviewer cannot find
the author described their process of validation. The readers would like to know the process
of training and validation comprehensively. Did the author divide their data from training and
validating? The author mentioned Monte Carlo methods in table_4 and the reviewer cannot find
the description of how the author performed the simulation.
The author included 300 cases of BA cohort in their cohort. In table_5, the reviewer finds
the colinearity of the data (CDCA=LDCA+HDCA+UDCA). If the author put all the CDCA,
LDCA, HDCA, and UDCA in the data matrix, the statistical inference would be misleading in
a regression model.
The author utilized r,  python, and their related packages for treating missing values, modeling,
and validating. The packages and their versions should be stated.  It would be better when the
author demonstrated their processes step by step in a Jupyter Notebook.
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 2 Date: 05 Dec 2022
Reviewer's report:
From line 112 to line 117, the defined diagnosis of cholangiocarcinoma includes cases without
histopathology or cytopathology. The authors included patients who would not be treated as
cholangiocarcinoma in clinical practice.
In the manuscript, the CA19-9 was used to diagnose and predict cholangiocarcinoma; the
reviewer thinks the process is not logically appropriate.
The study aimed at the prediction of cholangiocarcinoma among PSC patients. In line 126, the
author included medical records of healthy controls; the reviewer cannot find them in the flow
chart of the supplementation material. Have the health controls been analyzed?
Table_4 demonstrates the predictive model of the baseline cohort; the reviewer cannot find
the author described their process of validation. The readers would like to know the process
of training and validation comprehensively. Did the author divide their data from training and
validating? The author mentioned Monte Carlo methods in table_4 and the reviewer cannot find
the description of how the author performed the simulation.
The author included 300 cases of BA cohort in their cohort. In table_5, the reviewer finds
the colinearity of the data (CDCA=LDCA+HDCA+UDCA). If the author put all the CDCA,
LDCA, HDCA, and UDCA in the data matrix, the statistical inference would be misleading in
a regression model.
The author utilized r,  python, and their related packages for treating missing values, modeling,
and validating. The packages and their versions should be stated.  It would be better when the
author demonstrated their processes step by step in a Jupyter Notebook.
Please review this paper.
user
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 2 Date: 05 Dec 2022
Reviewer's report:
From line 112 to line 117, the defined diagnosis of cholangiocarcinoma includes cases without
histopathology or cytopathology. The authors included patients who would not be treated as
cholangiocarcinoma in clinical practice.
In the manuscript, the CA19-9 was used to diagnose and predict cholangiocarcinoma; the
reviewer thinks the process is not logically appropriate.
The study aimed at the prediction of cholangiocarcinoma among PSC patients. In line 126, the
author included medical records of healthy controls; the reviewer cannot find them in the flow
chart of the supplementation material. Have the health controls been analyzed?
Table_4 demonstrates the predictive model of the baseline cohort; the reviewer cannot find
the author described their process of validation. The readers would like to know the process
of training and validation comprehensively. Did the author divide their data from training and
validating? The author mentioned Monte Carlo methods in table_4 and the reviewer cannot find
the description of how the author performed the simulation.
The author included 300 cases of BA cohort in their cohort. In table_5, the reviewer finds
the colinearity of the data (CDCA=LDCA+HDCA+UDCA). If the author put all the CDCA,
LDCA, HDCA, and UDCA in the data matrix, the statistical inference would be misleading in
a regression model.
The author utilized r,  python, and their related packages for treating missing values, modeling,
and validating. The packages and their versions should be stated.  It would be better when the
author demonstrated their processes step by step in a Jupyter Notebook.
assistant