Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 05 Mar 2025
Reviewer's report:
Summary: This paper presents a more accessible solution for identifying powerful biomarkers
in gastrointestinal cancer using immunohistochemistry (IHC). The core approach involves
virtually staining H&E whole-slide images (WSI) with IHC using artificial intelligence (AI). The
performance of the proposed method is evaluated using five standard quantifiable biomarkers
and three expert pathologists.
Strengths:
- The research is well-motivated and clinically relevant.
- The involvement of three pathologists in comparing virtual and real IHC images adds an
interesting validation angle.
- The method is simple and easy to understand.
- Figure 5 effectively demonstrates that AI-generated IHC is as reliable as real IHC for predicting
biomarkers, which is a very compelling finding.
Minor issues:
- Table S2 suggests that the pathologists knew whether the image was virtual IHC or real. This
awareness could introduce bias into their evaluations. The authors should address this potential
bias in the results or discussion section.
- The sentence "The training sets and the testing sets were non-overlapping and randomly
allocated" is ambiguous. Given that multiple WSIs come from the same patient, the train-test
split should ideally be patient-based. However, the current wording suggests it may have been
WSI-based, which could allow overlap between train and test sets from the same patient. This
should be clarified.
Minor adjustments:
- The terms "virtual IHC" and "AI-IHC" are used interchangeably. Consistency is important, so
it would be better to choose one term and use it consistently throughout the paper.
- In the section "Automatic annotation of target regions," "ten consecutive HE slides" should be
corrected to "ten consecutive H&E slides."
- The sub-subheading "Construction of five biomarker models" is unnecessary since there is
only one subsection under "Model development and test." It can be removed.
- The sentence "Ki-67LI: percentage of invasive cancer cell nuclei that are positive for Ki-67
immunostaining over total invasive cancer cell nuclei present in a histological sample" is too
long and important to be placed in parentheses. The Ki-67LI should be explained clearly in the
text, and it would be helpful to include a formula for clarity.
- The sentence "During this process, the time of pathology reviewing ranges from 14±8 minutes,
resulting in precise labeled tiles that make every cellular structural detail on the WSI." lacks
context for the time reference. Is 14 minutes faster than the usual review time? Including a
baseline or reference value would help clarify the significance of this figure.
Conclusion:
This study addresses a clinically significant problem and presents a well-rounded and
comprehensive method. The results are consistent with the proposed goals. With a few
clarifications and minor revisions, the paper is ready for publication.
Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 05 Mar 2025
Reviewer's report:
Summary: This paper presents a more accessible solution for identifying powerful biomarkers
in gastrointestinal cancer using immunohistochemistry (IHC). The core approach involves
virtually staining H&E whole-slide images (WSI) with IHC using artificial intelligence (AI). The
performance of the proposed method is evaluated using five standard quantifiable biomarkers
and three expert pathologists.
Strengths:
- The research is well-motivated and clinically relevant.
- The involvement of three pathologists in comparing virtual and real IHC images adds an
interesting validation angle.
- The method is simple and easy to understand.
- Figure 5 effectively demonstrates that AI-generated IHC is as reliable as real IHC for predicting
biomarkers, which is a very compelling finding.
Minor issues:
- Table S2 suggests that the pathologists knew whether the image was virtual IHC or real. This
awareness could introduce bias into their evaluations. The authors should address this potential
bias in the results or discussion section.
- The sentence "The training sets and the testing sets were non-overlapping and randomly
allocated" is ambiguous. Given that multiple WSIs come from the same patient, the train-test
split should ideally be patient-based. However, the current wording suggests it may have been
WSI-based, which could allow overlap between train and test sets from the same patient. This
should be clarified.
Minor adjustments:
- The terms "virtual IHC" and "AI-IHC" are used interchangeably. Consistency is important, so
it would be better to choose one term and use it consistently throughout the paper.
- In the section "Automatic annotation of target regions," "ten consecutive HE slides" should be
corrected to "ten consecutive H&E slides."
- The sub-subheading "Construction of five biomarker models" is unnecessary since there is
only one subsection under "Model development and test." It can be removed.
- The sentence "Ki-67LI: percentage of invasive cancer cell nuclei that are positive for Ki-67
immunostaining over total invasive cancer cell nuclei present in a histological sample" is too
long and important to be placed in parentheses. The Ki-67LI should be explained clearly in the
text, and it would be helpful to include a formula for clarity.
- The sentence "During this process, the time of pathology reviewing ranges from 14±8 minutes,
resulting in precise labeled tiles that make every cellular structural detail on the WSI." lacks
context for the time reference. Is 14 minutes faster than the usual review time? Including a
baseline or reference value would help clarify the significance of this figure.
Conclusion:
This study addresses a clinically significant problem and presents a well-rounded and
comprehensive method. The results are consistent with the proposed goals. With a few
clarifications and minor revisions, the paper is ready for publication.
Please review this paper.
user
Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 05 Mar 2025
Reviewer's report:
Summary: This paper presents a more accessible solution for identifying powerful biomarkers
in gastrointestinal cancer using immunohistochemistry (IHC). The core approach involves
virtually staining H&E whole-slide images (WSI) with IHC using artificial intelligence (AI). The
performance of the proposed method is evaluated using five standard quantifiable biomarkers
and three expert pathologists.
Strengths:
- The research is well-motivated and clinically relevant.
- The involvement of three pathologists in comparing virtual and real IHC images adds an
interesting validation angle.
- The method is simple and easy to understand.
- Figure 5 effectively demonstrates that AI-generated IHC is as reliable as real IHC for predicting
biomarkers, which is a very compelling finding.
Minor issues:
- Table S2 suggests that the pathologists knew whether the image was virtual IHC or real. This
awareness could introduce bias into their evaluations. The authors should address this potential
bias in the results or discussion section.
- The sentence "The training sets and the testing sets were non-overlapping and randomly
allocated" is ambiguous. Given that multiple WSIs come from the same patient, the train-test
split should ideally be patient-based. However, the current wording suggests it may have been
WSI-based, which could allow overlap between train and test sets from the same patient. This
should be clarified.
Minor adjustments:
- The terms "virtual IHC" and "AI-IHC" are used interchangeably. Consistency is important, so
it would be better to choose one term and use it consistently throughout the paper.
- In the section "Automatic annotation of target regions," "ten consecutive HE slides" should be
corrected to "ten consecutive H&E slides."
- The sub-subheading "Construction of five biomarker models" is unnecessary since there is
only one subsection under "Model development and test." It can be removed.
- The sentence "Ki-67LI: percentage of invasive cancer cell nuclei that are positive for Ki-67
immunostaining over total invasive cancer cell nuclei present in a histological sample" is too
long and important to be placed in parentheses. The Ki-67LI should be explained clearly in the
text, and it would be helpful to include a formula for clarity.
- The sentence "During this process, the time of pathology reviewing ranges from 14±8 minutes,
resulting in precise labeled tiles that make every cellular structural detail on the WSI." lacks
context for the time reference. Is 14 minutes faster than the usual review time? Including a
baseline or reference value would help clarify the significance of this figure.
Conclusion:
This study addresses a clinically significant problem and presents a well-rounded and
comprehensive method. The results are consistent with the proposed goals. With a few
clarifications and minor revisions, the paper is ready for publication.
assistant