Title: Interpretable machine learning-based clinical prediction model for predicting lymph node
metastasis in patients with intrahepatic cholangiocarcinoma
Version: 1 Date: 19 Feb 2024
Reviewer:  Lujun Shen
Reviewer's report:
Overall, this study is well-conducted and provides valuable insights into the prediction of
lymph node metastasis in patients with intrahepatic cholangiocarcinoma using machine learning
algorithms. The methodology is sound, and the results are clearly presented. However, there are
several areas that could be improved:
As mentioned in the paper, the study lacks an analysis of lymph node metastasis by neutrophils,
lymphocytes, and monocytes. Including them in the analysis might improve the accuracy of the
prediction model. Furthermore, This study is retrospective and from a single institution, which
may limit the generalizability of the findings. It would be beneficial to validate the models using
an external dataset or in a multi-center study.
The research could benefit from a more thorough discussion on why alcoholic liver disease was
found to be a protective factor for LNM, as this is counterintuitive and contrary to previous
research.
Title: Interpretable machine learning-based clinical prediction model for predicting lymph node
metastasis in patients with intrahepatic cholangiocarcinoma
Version: 1 Date: 19 Feb 2024
Reviewer:  Lujun Shen
Reviewer's report:
Overall, this study is well-conducted and provides valuable insights into the prediction of
lymph node metastasis in patients with intrahepatic cholangiocarcinoma using machine learning
algorithms. The methodology is sound, and the results are clearly presented. However, there are
several areas that could be improved:
As mentioned in the paper, the study lacks an analysis of lymph node metastasis by neutrophils,
lymphocytes, and monocytes. Including them in the analysis might improve the accuracy of the
prediction model. Furthermore, This study is retrospective and from a single institution, which
may limit the generalizability of the findings. It would be beneficial to validate the models using
an external dataset or in a multi-center study.
The research could benefit from a more thorough discussion on why alcoholic liver disease was
found to be a protective factor for LNM, as this is counterintuitive and contrary to previous
research.
Please review this paper.
user
Title: Interpretable machine learning-based clinical prediction model for predicting lymph node
metastasis in patients with intrahepatic cholangiocarcinoma
Version: 1 Date: 19 Feb 2024
Reviewer:  Lujun Shen
Reviewer's report:
Overall, this study is well-conducted and provides valuable insights into the prediction of
lymph node metastasis in patients with intrahepatic cholangiocarcinoma using machine learning
algorithms. The methodology is sound, and the results are clearly presented. However, there are
several areas that could be improved:
As mentioned in the paper, the study lacks an analysis of lymph node metastasis by neutrophils,
lymphocytes, and monocytes. Including them in the analysis might improve the accuracy of the
prediction model. Furthermore, This study is retrospective and from a single institution, which
may limit the generalizability of the findings. It would be beneficial to validate the models using
an external dataset or in a multi-center study.
The research could benefit from a more thorough discussion on why alcoholic liver disease was
found to be a protective factor for LNM, as this is counterintuitive and contrary to previous
research.
assistant