Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 14 Mar 2025
Reviewer's report:
Overall Comments:
This manuscript presents a well-designed study that leverages machine learning to predict
metastasis in colorectal cancer patients. The inclusion of diverse factors (demographic, lifestyle,
nutritional, and clinical) is a notable strength, as it enhances the model's predictive power and
clinical relevance. The use of external validation and interpretability techniques (SHAP, PFI)
further strengthens the study's credibility. The manuscript is generally well-written and makes
a valuable contribution to the field.
However, addressing the following minor concerns could significantly improve the quality and
readability of the manuscript:
Minor Concerns:
The abstract lacks specific details about the external validation cohort, which are critical for
understanding the study's generalizability and clinical applicability.
The current keywords (Colorectal cancer, metastasis, prognosis, machine learning, prediction
model, clinical usability) are appropriate but could be refined. Removing "clinical usability" and
replacing it with "risk factors" or "lifestyle factors" would better reflect the study's focus and
improve discoverability.
In Introduction section, strengthen the rationale for including lifestyle and nutritional factors
by referencing studies that highlight their role in CRC progression, and briefly mention the
limitations of previous ML studies to set the stage for the current work.
In Methods section, add a flowchart illustrating model development pipeline.
Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 14 Mar 2025
Reviewer's report:
Overall Comments:
This manuscript presents a well-designed study that leverages machine learning to predict
metastasis in colorectal cancer patients. The inclusion of diverse factors (demographic, lifestyle,
nutritional, and clinical) is a notable strength, as it enhances the model's predictive power and
clinical relevance. The use of external validation and interpretability techniques (SHAP, PFI)
further strengthens the study's credibility. The manuscript is generally well-written and makes
a valuable contribution to the field.
However, addressing the following minor concerns could significantly improve the quality and
readability of the manuscript:
Minor Concerns:
The abstract lacks specific details about the external validation cohort, which are critical for
understanding the study's generalizability and clinical applicability.
The current keywords (Colorectal cancer, metastasis, prognosis, machine learning, prediction
model, clinical usability) are appropriate but could be refined. Removing "clinical usability" and
replacing it with "risk factors" or "lifestyle factors" would better reflect the study's focus and
improve discoverability.
In Introduction section, strengthen the rationale for including lifestyle and nutritional factors
by referencing studies that highlight their role in CRC progression, and briefly mention the
limitations of previous ML studies to set the stage for the current work.
In Methods section, add a flowchart illustrating model development pipeline.
Please review this paper.
user
Title: Optimizing prediction of metastasis among colorectal cancer patients using machine
learning technology
Version: 0 Date: 14 Mar 2025
Reviewer's report:
Overall Comments:
This manuscript presents a well-designed study that leverages machine learning to predict
metastasis in colorectal cancer patients. The inclusion of diverse factors (demographic, lifestyle,
nutritional, and clinical) is a notable strength, as it enhances the model's predictive power and
clinical relevance. The use of external validation and interpretability techniques (SHAP, PFI)
further strengthens the study's credibility. The manuscript is generally well-written and makes
a valuable contribution to the field.
However, addressing the following minor concerns could significantly improve the quality and
readability of the manuscript:
Minor Concerns:
The abstract lacks specific details about the external validation cohort, which are critical for
understanding the study's generalizability and clinical applicability.
The current keywords (Colorectal cancer, metastasis, prognosis, machine learning, prediction
model, clinical usability) are appropriate but could be refined. Removing "clinical usability" and
replacing it with "risk factors" or "lifestyle factors" would better reflect the study's focus and
improve discoverability.
In Introduction section, strengthen the rationale for including lifestyle and nutritional factors
by referencing studies that highlight their role in CRC progression, and briefly mention the
limitations of previous ML studies to set the stage for the current work.
In Methods section, add a flowchart illustrating model development pipeline.
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