Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 26 Mar 2025
Reviewer's report:
Colorectal cancer is a serious public health threat and is increasingly diagnosed among young
adults. Late diagnosis often occurs in the metastatic phase and prevents effective therapy. This
article addresses the difficult topic of predicting metastases in patients with this type of cancer.
The author has taken on a difficult and important topic. The research was well conducted and
the manuscript was prepared thoroughly. However, it requires minor corrections before it is
accepted for publication.
1) The abstract and the rest of the article mention F-score. This metric is more often called F1-
Score. Did the author mean the F1-score metric? Maybe for the sake of readability of the article
it would be worth presenting the patterns of the metrics used, e.g. in the "ML model construction
and assessment" section.
2) The literature review needs to be expanded, in particular, to
include newer works using machine learning methods in diagnostics,
survival prediction, metastasis in patients with colorectal cancer. Please
include the following articles in your review: https://www.sciencedirect.com/
science/article/pii/S2405844024174749 https://www.mdpi.com/2072-6694/16/18/3205 https://
www.nature.com/articles/s41598-023-35649-9 https://www.mdpi.com/2673-7426/4/1/41
https://bmccancer.biomedcentral.com/articles/10.1186/s12885-023-10587-x
3) The author wrote: "If each row had more than 10% missing data in its value, we deleted that
record from the current database. ". How many samples exactly were deleted?
4) The author used a number of well-known and modern classifiers in his research, such as
XgBoost or LightGBM. For the sake of completeness of the research, I think it is worth including
the KNN algorithm - although it most likely will not achieve better results.
5) How were the parameters of the individual classifiers selected? Was it decided to choose grid
search or the modern Optuna framework?
6) I think that the results shown in Figure 2 would be more readable if presented in table form.
In my opinion, after taking into account the above comments, the article will be acceptable.
Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 26 Mar 2025
Reviewer's report:
Colorectal cancer is a serious public health threat and is increasingly diagnosed among young
adults. Late diagnosis often occurs in the metastatic phase and prevents effective therapy. This
article addresses the difficult topic of predicting metastases in patients with this type of cancer.
The author has taken on a difficult and important topic. The research was well conducted and
the manuscript was prepared thoroughly. However, it requires minor corrections before it is
accepted for publication.
1) The abstract and the rest of the article mention F-score. This metric is more often called F1-
Score. Did the author mean the F1-score metric? Maybe for the sake of readability of the article
it would be worth presenting the patterns of the metrics used, e.g. in the "ML model construction
and assessment" section.
2) The literature review needs to be expanded, in particular, to
include newer works using machine learning methods in diagnostics,
survival prediction, metastasis in patients with colorectal cancer. Please
include the following articles in your review: https://www.sciencedirect.com/
science/article/pii/S2405844024174749 https://www.mdpi.com/2072-6694/16/18/3205 https://
www.nature.com/articles/s41598-023-35649-9 https://www.mdpi.com/2673-7426/4/1/41
https://bmccancer.biomedcentral.com/articles/10.1186/s12885-023-10587-x
3) The author wrote: "If each row had more than 10% missing data in its value, we deleted that
record from the current database. ". How many samples exactly were deleted?
4) The author used a number of well-known and modern classifiers in his research, such as
XgBoost or LightGBM. For the sake of completeness of the research, I think it is worth including
the KNN algorithm - although it most likely will not achieve better results.
5) How were the parameters of the individual classifiers selected? Was it decided to choose grid
search or the modern Optuna framework?
6) I think that the results shown in Figure 2 would be more readable if presented in table form.
In my opinion, after taking into account the above comments, the article will be acceptable.
Please review this paper.
user
Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 26 Mar 2025
Reviewer's report:
Colorectal cancer is a serious public health threat and is increasingly diagnosed among young
adults. Late diagnosis often occurs in the metastatic phase and prevents effective therapy. This
article addresses the difficult topic of predicting metastases in patients with this type of cancer.
The author has taken on a difficult and important topic. The research was well conducted and
the manuscript was prepared thoroughly. However, it requires minor corrections before it is
accepted for publication.
1) The abstract and the rest of the article mention F-score. This metric is more often called F1-
Score. Did the author mean the F1-score metric? Maybe for the sake of readability of the article
it would be worth presenting the patterns of the metrics used, e.g. in the "ML model construction
and assessment" section.
2) The literature review needs to be expanded, in particular, to
include newer works using machine learning methods in diagnostics,
survival prediction, metastasis in patients with colorectal cancer. Please
include the following articles in your review: https://www.sciencedirect.com/
science/article/pii/S2405844024174749 https://www.mdpi.com/2072-6694/16/18/3205 https://
www.nature.com/articles/s41598-023-35649-9 https://www.mdpi.com/2673-7426/4/1/41
https://bmccancer.biomedcentral.com/articles/10.1186/s12885-023-10587-x
3) The author wrote: "If each row had more than 10% missing data in its value, we deleted that
record from the current database. ". How many samples exactly were deleted?
4) The author used a number of well-known and modern classifiers in his research, such as
XgBoost or LightGBM. For the sake of completeness of the research, I think it is worth including
the KNN algorithm - although it most likely will not achieve better results.
5) How were the parameters of the individual classifiers selected? Was it decided to choose grid
search or the modern Optuna framework?
6) I think that the results shown in Figure 2 would be more readable if presented in table form.
In my opinion, after taking into account the above comments, the article will be acceptable.
assistant