Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 28 Feb 2025
Reviewer:  Giorgio Cazzaniga
Reviewer's report:
The study presents a tile classification experiment on H&E-stained slides based on the
predicted positivity or negativity of five IHC stains in gastrointestinal neoplastic pathology. The
manuscript is well-written, and its primary strength lies in the comparison with pathologists'
evaluations.
However, several major issues should be addressed:
Title: The current title is misleading, as it suggests a virtual staining study, which is not actually
performed. I recommend referring instead to an "IHC prediction model."
Debatable statements:
The claim that IHC is expensive should be reconsidered. Immunohistochemistry is generally
classified as a low-cost technique, especially when performed according to standard guidelines.
The assertion that the WHO 5th edition recommends these five biomarkers and considers them
essential is too general. The necessity of specific biomarkers varies on a case-by-case basis.
Additionally, these markers do not appear to be the most commonly used or the most relevant
in gastrointestinal carcinomas. The rationale behind their selection should be justified.
The claim that the proposed model is "not inferior" to classical IHC assessment should
be moderated. Unlike IHC, which provides quantitative values and allows for background
visualization—enabling a meaningful distinction between positive structures and their degree of
positivity—this model does not account for such differences. For instance, in a crucial diagnostic
marker like p53, different expression patterns (wild-type, overexpression, null-type) were not
explicitly considered. Furthermore, the reported accuracy at the patch level deviates by 10–20%
from classical IHC evaluation, which does not support the claim of non-inferiority.
Methodological concerns:
It is unclear how marker positivity was defined for translation to H&E staining and subsequent
patch classification. A more detailed explanation of the methods should be provided.
The evaluation of the T stage is only briefly mentioned in the methods, and it is unclear how it
was determined. Further methodological details should be included.
Only two cases were used to assess variation in overlay alignment across consecutive sections,
which is not a sufficiently generalizable sample. This limitation should be justified. Additionally,
the lack of non-rigid slide registration methods should be explained, and the thickness of
consecutive sections should be specified. The methodology and results regarding slide alignment
are not clearly described, particularly the significance of the mutual information value, which
should be better explained.
There is no mention of the resolution of WSIs in terms of microns per pixel or the magnification
at which patches were extracted (was it at native resolution?). This aspect is crucial, as different
resolutions can significantly impact the results. Lower-resolution patches encompass a larger
number of cells and may artificially "enrich" the positive patch category in the training/test set.
Conversely, higher resolution (ideally isolating individual cells) allows for a more precise patch
distribution, affecting both training and testing. Including graphical examples of patches would
be beneficial.
Other concerns:
The study does not present results from the questionnaire, which is only briefly mentioned in
the discussion. This section does not appear to be particularly relevant to the study and should
be reconsidered.
The analyzed cases come from a limited patient sample, which must be explicitly acknowledged
in the study limitations.
Supplementary Table 3 is important and, if possible, should be moved to the main manuscript
in a condensed form.
The acronym "MRMC study" should be explicitly defined in the abstract.
The acronym "WURH" should be explicitly defined in the introduction.
Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 28 Feb 2025
Reviewer:  Giorgio Cazzaniga
Reviewer's report:
The study presents a tile classification experiment on H&E-stained slides based on the
predicted positivity or negativity of five IHC stains in gastrointestinal neoplastic pathology. The
manuscript is well-written, and its primary strength lies in the comparison with pathologists'
evaluations.
However, several major issues should be addressed:
Title: The current title is misleading, as it suggests a virtual staining study, which is not actually
performed. I recommend referring instead to an "IHC prediction model."
Debatable statements:
The claim that IHC is expensive should be reconsidered. Immunohistochemistry is generally
classified as a low-cost technique, especially when performed according to standard guidelines.
The assertion that the WHO 5th edition recommends these five biomarkers and considers them
essential is too general. The necessity of specific biomarkers varies on a case-by-case basis.
Additionally, these markers do not appear to be the most commonly used or the most relevant
in gastrointestinal carcinomas. The rationale behind their selection should be justified.
The claim that the proposed model is "not inferior" to classical IHC assessment should
be moderated. Unlike IHC, which provides quantitative values and allows for background
visualization—enabling a meaningful distinction between positive structures and their degree of
positivity—this model does not account for such differences. For instance, in a crucial diagnostic
marker like p53, different expression patterns (wild-type, overexpression, null-type) were not
explicitly considered. Furthermore, the reported accuracy at the patch level deviates by 10–20%
from classical IHC evaluation, which does not support the claim of non-inferiority.
Methodological concerns:
It is unclear how marker positivity was defined for translation to H&E staining and subsequent
patch classification. A more detailed explanation of the methods should be provided.
The evaluation of the T stage is only briefly mentioned in the methods, and it is unclear how it
was determined. Further methodological details should be included.
Only two cases were used to assess variation in overlay alignment across consecutive sections,
which is not a sufficiently generalizable sample. This limitation should be justified. Additionally,
the lack of non-rigid slide registration methods should be explained, and the thickness of
consecutive sections should be specified. The methodology and results regarding slide alignment
are not clearly described, particularly the significance of the mutual information value, which
should be better explained.
There is no mention of the resolution of WSIs in terms of microns per pixel or the magnification
at which patches were extracted (was it at native resolution?). This aspect is crucial, as different
resolutions can significantly impact the results. Lower-resolution patches encompass a larger
number of cells and may artificially "enrich" the positive patch category in the training/test set.
Conversely, higher resolution (ideally isolating individual cells) allows for a more precise patch
distribution, affecting both training and testing. Including graphical examples of patches would
be beneficial.
Other concerns:
The study does not present results from the questionnaire, which is only briefly mentioned in
the discussion. This section does not appear to be particularly relevant to the study and should
be reconsidered.
The analyzed cases come from a limited patient sample, which must be explicitly acknowledged
in the study limitations.
Supplementary Table 3 is important and, if possible, should be moved to the main manuscript
in a condensed form.
The acronym "MRMC study" should be explicitly defined in the abstract.
The acronym "WURH" should be explicitly defined in the introduction.
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: 28 Feb 2025
Reviewer:  Giorgio Cazzaniga
Reviewer's report:
The study presents a tile classification experiment on H&E-stained slides based on the
predicted positivity or negativity of five IHC stains in gastrointestinal neoplastic pathology. The
manuscript is well-written, and its primary strength lies in the comparison with pathologists'
evaluations.
However, several major issues should be addressed:
Title: The current title is misleading, as it suggests a virtual staining study, which is not actually
performed. I recommend referring instead to an "IHC prediction model."
Debatable statements:
The claim that IHC is expensive should be reconsidered. Immunohistochemistry is generally
classified as a low-cost technique, especially when performed according to standard guidelines.
The assertion that the WHO 5th edition recommends these five biomarkers and considers them
essential is too general. The necessity of specific biomarkers varies on a case-by-case basis.
Additionally, these markers do not appear to be the most commonly used or the most relevant
in gastrointestinal carcinomas. The rationale behind their selection should be justified.
The claim that the proposed model is "not inferior" to classical IHC assessment should
be moderated. Unlike IHC, which provides quantitative values and allows for background
visualization—enabling a meaningful distinction between positive structures and their degree of
positivity—this model does not account for such differences. For instance, in a crucial diagnostic
marker like p53, different expression patterns (wild-type, overexpression, null-type) were not
explicitly considered. Furthermore, the reported accuracy at the patch level deviates by 10–20%
from classical IHC evaluation, which does not support the claim of non-inferiority.
Methodological concerns:
It is unclear how marker positivity was defined for translation to H&E staining and subsequent
patch classification. A more detailed explanation of the methods should be provided.
The evaluation of the T stage is only briefly mentioned in the methods, and it is unclear how it
was determined. Further methodological details should be included.
Only two cases were used to assess variation in overlay alignment across consecutive sections,
which is not a sufficiently generalizable sample. This limitation should be justified. Additionally,
the lack of non-rigid slide registration methods should be explained, and the thickness of
consecutive sections should be specified. The methodology and results regarding slide alignment
are not clearly described, particularly the significance of the mutual information value, which
should be better explained.
There is no mention of the resolution of WSIs in terms of microns per pixel or the magnification
at which patches were extracted (was it at native resolution?). This aspect is crucial, as different
resolutions can significantly impact the results. Lower-resolution patches encompass a larger
number of cells and may artificially "enrich" the positive patch category in the training/test set.
Conversely, higher resolution (ideally isolating individual cells) allows for a more precise patch
distribution, affecting both training and testing. Including graphical examples of patches would
be beneficial.
Other concerns:
The study does not present results from the questionnaire, which is only briefly mentioned in
the discussion. This section does not appear to be particularly relevant to the study and should
be reconsidered.
The analyzed cases come from a limited patient sample, which must be explicitly acknowledged
in the study limitations.
Supplementary Table 3 is important and, if possible, should be moved to the main manuscript
in a condensed form.
The acronym "MRMC study" should be explicitly defined in the abstract.
The acronym "WURH" should be explicitly defined in the introduction.
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