Title: Interpretable machine learning-based clinical prediction model for predicting lymph node
metastasis in patients with intrahepatic cholangiocarcinoma
Version: 1 Date: 23 Feb 2024
Reviewer's report:
This is meaningful   well-designed study which established a web calculator  to assist clinicians
to predict LNM and developed individualized medical plans. Artificial intelligence shows
promising future in the field of medicine and radiology.
However, the variables were not adequate like  CT value,  b value of DWI, the  max diameter of
lymph node in the radiologic images, the differentiation degree of  tumor, tumor markers and
et al.  The variables are also the important factors which may related to LNM. So i suggest you
to add these  variables to this a machine learning algorithm, which could make your conclusion
more  exact.
Title: Interpretable machine learning-based clinical prediction model for predicting lymph node
metastasis in patients with intrahepatic cholangiocarcinoma
Version: 1 Date: 23 Feb 2024
Reviewer's report:
This is meaningful   well-designed study which established a web calculator  to assist clinicians
to predict LNM and developed individualized medical plans. Artificial intelligence shows
promising future in the field of medicine and radiology.
However, the variables were not adequate like  CT value,  b value of DWI, the  max diameter of
lymph node in the radiologic images, the differentiation degree of  tumor, tumor markers and
et al.  The variables are also the important factors which may related to LNM. So i suggest you
to add these  variables to this a machine learning algorithm, which could make your conclusion
more  exact.
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: 23 Feb 2024
Reviewer's report:
This is meaningful   well-designed study which established a web calculator  to assist clinicians
to predict LNM and developed individualized medical plans. Artificial intelligence shows
promising future in the field of medicine and radiology.
However, the variables were not adequate like  CT value,  b value of DWI, the  max diameter of
lymph node in the radiologic images, the differentiation degree of  tumor, tumor markers and
et al.  The variables are also the important factors which may related to LNM. So i suggest you
to add these  variables to this a machine learning algorithm, which could make your conclusion
more  exact.
assistant