Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 27 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The paper mentions the use of semi-supervised deep learning for model training, but further
details on the model architecture (e.g., type of deep learning models, layers, loss functions,
and optimization methods) would help readers better understand the modeling approach and its
potential for reproducibility. Detailed model parameters would enable researchers to build upon
this work in the future.
-While the study mentions the use of HEMnet for label transfer, the issue of requiring adjacency
between IHC and H&E slides is not fully addressed in terms of its practical limitations. How
often does this limitation affect the clinical applicability, and how significant is the impact of
non-adjacent slides in practice? Providing more clarity on how often these situations occur and
how the model compensates for this limitation would strengthen the paper.
-There is a noted variability in the Ki-67 proliferation index (17.35% ± 16.2%) between AI-IHC
and Real-IHC. This result requires further exploration. What specific factors contribute to this
variability? Are there any aspects of the virtual IHC model that could be further optimized to
reduce this variability? A deeper analysis of this discrepancy would provide valuable insight
into the potential clinical implications of the model in its current form.
-The study is based on data from a single hospital, which may limit the generalizability of the
findings. It would be beneficial to conduct validation across multiple institutions or with a more
diverse cohort to ensure that the model is applicable across various clinical settings and patient
demographics. This could be particularly important for biomarkers like Ki-67, where variations
in patient population may affect model performance.
-While the study demonstrates the feasibility of AI-based virtual IHC, it would be helpful
to include more discussion on how such a model could be integrated into existing clinical
workflows. What challenges might arise in implementing AI-based IHC in routine practice?
How would pathologists adapt to using these virtual results, and what kind of support or
validation might be needed to integrate these tools effectively into daily pathology work?
-The discussion mentions that the P53 biomarker model performed less robustly compared to
others, and this is attributed to the "relatively weaker correlation between protein expression and
cellular morphology." However, the authors could further explore this observation. Are there
specific characteristics of the P53 protein expression that make it harder for the AI model to
predict accurately? Is there a known variability in P53 expression patterns that might affect
model performance? Further analysis of the limitations in predicting P53 could be valuable in
guiding future improvements for this biomarker model.
-The authors acknowledge the limitation of using a single institution's dataset, which can limit
the generalizability of the findings. While this is a common issue in early-stage AI research,
the discussion could benefit from more detailed suggestions for overcoming this limitation. For
example, how could the model be adapted or trained on data from multiple centers to ensure its
robustness across diverse patient populations? Discussing potential solutions or plans to address
this challenge would strengthen the argument for broader applicability.
-While the study focuses on gastrointestinal cancers, the potential for expanding this model
to other cancer types is not discussed in detail. Given the wide-ranging applications of IHC
in oncology, it would be interesting to explore how this model could be adapted for other
types of cancer. Are there significant differences in biomarker expression or tissue morphology
across cancer types that could affect the model’s performance? A brief mention of plans or
considerations for expanding the model beyond GI cancers would add depth to the discussion.
-The pathologists in the MRMC study acknowledged the presence of false positives and
negatives in conventional IHC. The discussion could delve deeper into the potential sources of
false positives and negatives in the AI-IHC model and how the model could be improved to
minimize these issues. Are there specific biomarkers where false positives/negatives are more
frequent, and what strategies can be employed to address these challenges in future model
iterations?
Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 27 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The paper mentions the use of semi-supervised deep learning for model training, but further
details on the model architecture (e.g., type of deep learning models, layers, loss functions,
and optimization methods) would help readers better understand the modeling approach and its
potential for reproducibility. Detailed model parameters would enable researchers to build upon
this work in the future.
-While the study mentions the use of HEMnet for label transfer, the issue of requiring adjacency
between IHC and H&E slides is not fully addressed in terms of its practical limitations. How
often does this limitation affect the clinical applicability, and how significant is the impact of
non-adjacent slides in practice? Providing more clarity on how often these situations occur and
how the model compensates for this limitation would strengthen the paper.
-There is a noted variability in the Ki-67 proliferation index (17.35% ± 16.2%) between AI-IHC
and Real-IHC. This result requires further exploration. What specific factors contribute to this
variability? Are there any aspects of the virtual IHC model that could be further optimized to
reduce this variability? A deeper analysis of this discrepancy would provide valuable insight
into the potential clinical implications of the model in its current form.
-The study is based on data from a single hospital, which may limit the generalizability of the
findings. It would be beneficial to conduct validation across multiple institutions or with a more
diverse cohort to ensure that the model is applicable across various clinical settings and patient
demographics. This could be particularly important for biomarkers like Ki-67, where variations
in patient population may affect model performance.
-While the study demonstrates the feasibility of AI-based virtual IHC, it would be helpful
to include more discussion on how such a model could be integrated into existing clinical
workflows. What challenges might arise in implementing AI-based IHC in routine practice?
How would pathologists adapt to using these virtual results, and what kind of support or
validation might be needed to integrate these tools effectively into daily pathology work?
-The discussion mentions that the P53 biomarker model performed less robustly compared to
others, and this is attributed to the "relatively weaker correlation between protein expression and
cellular morphology." However, the authors could further explore this observation. Are there
specific characteristics of the P53 protein expression that make it harder for the AI model to
predict accurately? Is there a known variability in P53 expression patterns that might affect
model performance? Further analysis of the limitations in predicting P53 could be valuable in
guiding future improvements for this biomarker model.
-The authors acknowledge the limitation of using a single institution's dataset, which can limit
the generalizability of the findings. While this is a common issue in early-stage AI research,
the discussion could benefit from more detailed suggestions for overcoming this limitation. For
example, how could the model be adapted or trained on data from multiple centers to ensure its
robustness across diverse patient populations? Discussing potential solutions or plans to address
this challenge would strengthen the argument for broader applicability.
-While the study focuses on gastrointestinal cancers, the potential for expanding this model
to other cancer types is not discussed in detail. Given the wide-ranging applications of IHC
in oncology, it would be interesting to explore how this model could be adapted for other
types of cancer. Are there significant differences in biomarker expression or tissue morphology
across cancer types that could affect the model’s performance? A brief mention of plans or
considerations for expanding the model beyond GI cancers would add depth to the discussion.
-The pathologists in the MRMC study acknowledged the presence of false positives and
negatives in conventional IHC. The discussion could delve deeper into the potential sources of
false positives and negatives in the AI-IHC model and how the model could be improved to
minimize these issues. Are there specific biomarkers where false positives/negatives are more
frequent, and what strategies can be employed to address these challenges in future model
iterations?
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: 27 Feb 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The paper mentions the use of semi-supervised deep learning for model training, but further
details on the model architecture (e.g., type of deep learning models, layers, loss functions,
and optimization methods) would help readers better understand the modeling approach and its
potential for reproducibility. Detailed model parameters would enable researchers to build upon
this work in the future.
-While the study mentions the use of HEMnet for label transfer, the issue of requiring adjacency
between IHC and H&E slides is not fully addressed in terms of its practical limitations. How
often does this limitation affect the clinical applicability, and how significant is the impact of
non-adjacent slides in practice? Providing more clarity on how often these situations occur and
how the model compensates for this limitation would strengthen the paper.
-There is a noted variability in the Ki-67 proliferation index (17.35% ± 16.2%) between AI-IHC
and Real-IHC. This result requires further exploration. What specific factors contribute to this
variability? Are there any aspects of the virtual IHC model that could be further optimized to
reduce this variability? A deeper analysis of this discrepancy would provide valuable insight
into the potential clinical implications of the model in its current form.
-The study is based on data from a single hospital, which may limit the generalizability of the
findings. It would be beneficial to conduct validation across multiple institutions or with a more
diverse cohort to ensure that the model is applicable across various clinical settings and patient
demographics. This could be particularly important for biomarkers like Ki-67, where variations
in patient population may affect model performance.
-While the study demonstrates the feasibility of AI-based virtual IHC, it would be helpful
to include more discussion on how such a model could be integrated into existing clinical
workflows. What challenges might arise in implementing AI-based IHC in routine practice?
How would pathologists adapt to using these virtual results, and what kind of support or
validation might be needed to integrate these tools effectively into daily pathology work?
-The discussion mentions that the P53 biomarker model performed less robustly compared to
others, and this is attributed to the "relatively weaker correlation between protein expression and
cellular morphology." However, the authors could further explore this observation. Are there
specific characteristics of the P53 protein expression that make it harder for the AI model to
predict accurately? Is there a known variability in P53 expression patterns that might affect
model performance? Further analysis of the limitations in predicting P53 could be valuable in
guiding future improvements for this biomarker model.
-The authors acknowledge the limitation of using a single institution's dataset, which can limit
the generalizability of the findings. While this is a common issue in early-stage AI research,
the discussion could benefit from more detailed suggestions for overcoming this limitation. For
example, how could the model be adapted or trained on data from multiple centers to ensure its
robustness across diverse patient populations? Discussing potential solutions or plans to address
this challenge would strengthen the argument for broader applicability.
-While the study focuses on gastrointestinal cancers, the potential for expanding this model
to other cancer types is not discussed in detail. Given the wide-ranging applications of IHC
in oncology, it would be interesting to explore how this model could be adapted for other
types of cancer. Are there significant differences in biomarker expression or tissue morphology
across cancer types that could affect the model’s performance? A brief mention of plans or
considerations for expanding the model beyond GI cancers would add depth to the discussion.
-The pathologists in the MRMC study acknowledged the presence of false positives and
negatives in conventional IHC. The discussion could delve deeper into the potential sources of
false positives and negatives in the AI-IHC model and how the model could be improved to
minimize these issues. Are there specific biomarkers where false positives/negatives are more
frequent, and what strategies can be employed to address these challenges in future model
iterations?
assistant