Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 17 Jan 2023
Reviewer's report:
The reviewer acknowledged the difficulty of diagnosing cholangiocarcinoma (CCA) in primary
sclerosing cholangitis (PSC). A predictive model uses safe, economical, and easily accessible
data to predict risky, expansive, and technique-demanding life-threatening diagnoses with
acceptable inaccuracy. The AASLD 2010 guideline (DOI 10.1002/hep.23294) recommended
treating CCA when clinical suspicion of CCA with elevated CA19-9 (>129U/ml) and malignant
appearing stricture. Recently, the AASLD guideline divided the diagnosis of CCA as probable
CCA or CCA  (DOI: 10.1002/hep.32771 ).  The role of CA19-9 in CCA diagnosis diminished.
The cohorts the author utilized are important because they can link clinical observation to the
diagnosis of CCA in PSC.  A model raising suspicion of CCA in PSC is invaluable and deserved
to share with readers of BMC gastroenterology.
In Table_1, the number of cases with IgG4 levels was 364. The diagnosis of PSC should exclude
secondary causes of sclerosing cholangitis, for example, IgG4-associated cholangitis. For the
diagnosis of PSC, the authors should have the results. The reviewer would like to know why
most of the IgG4 results were not presented.
The reviewer searches the manuscript and finds the keyword: 'artificial intelligence in the
Title (line_2), abstract (lines_27, line_42), and discussion (lines_437, line_439), respectively.
In contrast, random survival Forrest (RSF) was found 22 times; Gradient Boosting Survival
Analysis (GBSA) was found 16 times. Artificial intelligence involves computer programs to
function as human intelligence. Deep learning, Artificial neural networks, machine learning, and
statistics are examples of artificial intelligence(Gastroenterology 2020;158:76–94).  The author
is suggested to clarify their definition of artificial intelligence and make their work coherent
to avoid confusion of the reader between artificial intelligence and machine learning (RSF and
GBSA). The reviewer also suggests precise terminology for their method because potential
readers can reach their publication easily.
Fig_S2 revealed the cumulative incidence of cholangiocarcinoma and competing risks. At the
maximum follow-up period, more than 30% of cases were treated with liver transplants. In
line_297, the histopathology on the liver explant was positive for cholangiocarcinoma in 8
patients. The reviewer would like to know the indication of liver transplantation for these 8
patients. Was the CCA diagnosed before transplantation?
Fig_S1 revealed the flow chart of the study (the merge of two cohorts). The author is suggested
following the demonstration of the flow chart by the 2010 CONSORT statement.
In line_74, the author states: 'one of the largest single-center well-phenotyped PSC cohorts.'
However, line_92 says: 'All Mayo and non-Mayo medical charts were comprehensively
reviewed.' The reviewer was confused about whether the study was single-center or multi-center.
In line 293, the sentence is complicated.
The term 'pipeline' is not familiar to the readers whom BMC gastroenterology aims.
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 17 Jan 2023
Reviewer's report:
The reviewer acknowledged the difficulty of diagnosing cholangiocarcinoma (CCA) in primary
sclerosing cholangitis (PSC). A predictive model uses safe, economical, and easily accessible
data to predict risky, expansive, and technique-demanding life-threatening diagnoses with
acceptable inaccuracy. The AASLD 2010 guideline (DOI 10.1002/hep.23294) recommended
treating CCA when clinical suspicion of CCA with elevated CA19-9 (>129U/ml) and malignant
appearing stricture. Recently, the AASLD guideline divided the diagnosis of CCA as probable
CCA or CCA  (DOI: 10.1002/hep.32771 ).  The role of CA19-9 in CCA diagnosis diminished.
The cohorts the author utilized are important because they can link clinical observation to the
diagnosis of CCA in PSC.  A model raising suspicion of CCA in PSC is invaluable and deserved
to share with readers of BMC gastroenterology.
In Table_1, the number of cases with IgG4 levels was 364. The diagnosis of PSC should exclude
secondary causes of sclerosing cholangitis, for example, IgG4-associated cholangitis. For the
diagnosis of PSC, the authors should have the results. The reviewer would like to know why
most of the IgG4 results were not presented.
The reviewer searches the manuscript and finds the keyword: 'artificial intelligence in the
Title (line_2), abstract (lines_27, line_42), and discussion (lines_437, line_439), respectively.
In contrast, random survival Forrest (RSF) was found 22 times; Gradient Boosting Survival
Analysis (GBSA) was found 16 times. Artificial intelligence involves computer programs to
function as human intelligence. Deep learning, Artificial neural networks, machine learning, and
statistics are examples of artificial intelligence(Gastroenterology 2020;158:76–94).  The author
is suggested to clarify their definition of artificial intelligence and make their work coherent
to avoid confusion of the reader between artificial intelligence and machine learning (RSF and
GBSA). The reviewer also suggests precise terminology for their method because potential
readers can reach their publication easily.
Fig_S2 revealed the cumulative incidence of cholangiocarcinoma and competing risks. At the
maximum follow-up period, more than 30% of cases were treated with liver transplants. In
line_297, the histopathology on the liver explant was positive for cholangiocarcinoma in 8
patients. The reviewer would like to know the indication of liver transplantation for these 8
patients. Was the CCA diagnosed before transplantation?
Fig_S1 revealed the flow chart of the study (the merge of two cohorts). The author is suggested
following the demonstration of the flow chart by the 2010 CONSORT statement.
In line_74, the author states: 'one of the largest single-center well-phenotyped PSC cohorts.'
However, line_92 says: 'All Mayo and non-Mayo medical charts were comprehensively
reviewed.' The reviewer was confused about whether the study was single-center or multi-center.
In line 293, the sentence is complicated.
The term 'pipeline' is not familiar to the readers whom BMC gastroenterology aims.
Please review this paper.
user
Title: Predicting Cholangiocarcinoma in Primary Sclerosing Cholangitis: Using Artificial
Intelligence, Clinical and Laboratory Data
Version: 4 Date: 17 Jan 2023
Reviewer's report:
The reviewer acknowledged the difficulty of diagnosing cholangiocarcinoma (CCA) in primary
sclerosing cholangitis (PSC). A predictive model uses safe, economical, and easily accessible
data to predict risky, expansive, and technique-demanding life-threatening diagnoses with
acceptable inaccuracy. The AASLD 2010 guideline (DOI 10.1002/hep.23294) recommended
treating CCA when clinical suspicion of CCA with elevated CA19-9 (>129U/ml) and malignant
appearing stricture. Recently, the AASLD guideline divided the diagnosis of CCA as probable
CCA or CCA  (DOI: 10.1002/hep.32771 ).  The role of CA19-9 in CCA diagnosis diminished.
The cohorts the author utilized are important because they can link clinical observation to the
diagnosis of CCA in PSC.  A model raising suspicion of CCA in PSC is invaluable and deserved
to share with readers of BMC gastroenterology.
In Table_1, the number of cases with IgG4 levels was 364. The diagnosis of PSC should exclude
secondary causes of sclerosing cholangitis, for example, IgG4-associated cholangitis. For the
diagnosis of PSC, the authors should have the results. The reviewer would like to know why
most of the IgG4 results were not presented.
The reviewer searches the manuscript and finds the keyword: 'artificial intelligence in the
Title (line_2), abstract (lines_27, line_42), and discussion (lines_437, line_439), respectively.
In contrast, random survival Forrest (RSF) was found 22 times; Gradient Boosting Survival
Analysis (GBSA) was found 16 times. Artificial intelligence involves computer programs to
function as human intelligence. Deep learning, Artificial neural networks, machine learning, and
statistics are examples of artificial intelligence(Gastroenterology 2020;158:76–94).  The author
is suggested to clarify their definition of artificial intelligence and make their work coherent
to avoid confusion of the reader between artificial intelligence and machine learning (RSF and
GBSA). The reviewer also suggests precise terminology for their method because potential
readers can reach their publication easily.
Fig_S2 revealed the cumulative incidence of cholangiocarcinoma and competing risks. At the
maximum follow-up period, more than 30% of cases were treated with liver transplants. In
line_297, the histopathology on the liver explant was positive for cholangiocarcinoma in 8
patients. The reviewer would like to know the indication of liver transplantation for these 8
patients. Was the CCA diagnosed before transplantation?
Fig_S1 revealed the flow chart of the study (the merge of two cohorts). The author is suggested
following the demonstration of the flow chart by the 2010 CONSORT statement.
In line_74, the author states: 'one of the largest single-center well-phenotyped PSC cohorts.'
However, line_92 says: 'All Mayo and non-Mayo medical charts were comprehensively
reviewed.' The reviewer was confused about whether the study was single-center or multi-center.
In line 293, the sentence is complicated.
The term 'pipeline' is not familiar to the readers whom BMC gastroenterology aims.
assistant