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's report:
Major issues:
1. "Five biomarker prediction models (P40, Pan-CK, Desmin, P53, Ki-67) were developed
following WHO's fifth tumor classification, with quantification of Ki-67 proliferation index."
Those are immunohistochemistry reactions in the bracket, why are they phrased as biomarker
prediction models?
2. Why are these reactions chosen? These are completely different IHC reaction, with totally
different interpretation.
3. FigS1A is not necessary.
4. The cover letter provided for the revision is not professional, and does not include responeses,
even though that is its title.
5. The English grammar of the article needs major revisions.
6. "With widespread adoption, the labor intensity, time consuming, the shortage of pathologists
and tissue costs present inherent limitations."
What does this sentence mean? The reactions will always be delivered by technicians, therefore,
it would never be replaced by AI.
The interpretation of IHC slides takes way less time every day then it would be beneficial to
use AI for that.
And if AI is initiated for the interpretation, and AI makes a mistake, who takes the responsibility?
Therefore, AI would never be able to replace a pathologist. So instead of FOMO, I recommend
to do science that actually helps diagnostics in the long run.
7. When an IHC reaction is initiated, there has to be positive control to see whether the reaction
is working at all or not. This was not included in this study, therefore, we are not even certain
whether these reactions worked properly, or not...
And if there were positive control on all slides, how would AI differentiate it from true positivity?
8. The KI-67 interpretation is very superficial and not professional at all.
9. "The fifth edition of the WHO recommends routinely investigating these five fundamental
biomarkers in GI cancer for pathology clinical practice: Pan-CK, P40, Desmin, P53, Ki-67."
These markers are used for completely different tumours. What does WHO recommendation
mean?!
10. "Using the mutual information values of the 1st to 2nd slides (0.658 for WSIs-1; 0.716 for
WSIs-2) as a reference, the mutual information values of the 1st to 3rd, 1st to 4th... 1st to 10th in
succession show a negligible decrease, with the decline ranging from 0.054 to 0.132 for WSIs-1
and 0.067 to 0.135 for WSIs-2 (Figure S1). "
This sentence has no proper meaning.
11. "Trained based on the precise labeled tiles, the accuracy of five biomarkers models in
the testing sets for predicting PAN-CK, P40, Desmin, P53, and Ki-67 reached 90.81% (95%
CI: 90.38%-91.23%), 88.66% (95% CI: 88.25%-89.06%), 89.16% (95% CI: 88.65%-89.95%),
83.04% (95% CI: 82.51%-83.55%), and 86.77% (95% CI: 86.35%-87.17%), respectively (Table
1)."
What are these results compared to? What did these examiners identified as correct?
12. Besides Kappa statistics, intraclass correlation should be initiated, as well.
13. Real and virtual IHC are not proper terms for an article!
14. p53 results: Kappa=0.379
This is not a good results, however,  in the discussion, the model is mentioned to be amazing.
It is not.
15. The interpratation of the Kappa statistics is not mentioned in the article at all.
16. It is not clear at all how T stage was evaluated.
17. It is mentioned nowhere what were the histological subtypes of the investigated tumors. That
is a major problem.
18. The list of abbreviations is completely unprofessional.
19. Why does the reference list has  a differing lettering? Unprofessional again.
20. Legends are short and unprofessional.
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's report:
Major issues:
1. "Five biomarker prediction models (P40, Pan-CK, Desmin, P53, Ki-67) were developed
following WHO's fifth tumor classification, with quantification of Ki-67 proliferation index."
Those are immunohistochemistry reactions in the bracket, why are they phrased as biomarker
prediction models?
2. Why are these reactions chosen? These are completely different IHC reaction, with totally
different interpretation.
3. FigS1A is not necessary.
4. The cover letter provided for the revision is not professional, and does not include responeses,
even though that is its title.
5. The English grammar of the article needs major revisions.
6. "With widespread adoption, the labor intensity, time consuming, the shortage of pathologists
and tissue costs present inherent limitations."
What does this sentence mean? The reactions will always be delivered by technicians, therefore,
it would never be replaced by AI.
The interpretation of IHC slides takes way less time every day then it would be beneficial to
use AI for that.
And if AI is initiated for the interpretation, and AI makes a mistake, who takes the responsibility?
Therefore, AI would never be able to replace a pathologist. So instead of FOMO, I recommend
to do science that actually helps diagnostics in the long run.
7. When an IHC reaction is initiated, there has to be positive control to see whether the reaction
is working at all or not. This was not included in this study, therefore, we are not even certain
whether these reactions worked properly, or not...
And if there were positive control on all slides, how would AI differentiate it from true positivity?
8. The KI-67 interpretation is very superficial and not professional at all.
9. "The fifth edition of the WHO recommends routinely investigating these five fundamental
biomarkers in GI cancer for pathology clinical practice: Pan-CK, P40, Desmin, P53, Ki-67."
These markers are used for completely different tumours. What does WHO recommendation
mean?!
10. "Using the mutual information values of the 1st to 2nd slides (0.658 for WSIs-1; 0.716 for
WSIs-2) as a reference, the mutual information values of the 1st to 3rd, 1st to 4th... 1st to 10th in
succession show a negligible decrease, with the decline ranging from 0.054 to 0.132 for WSIs-1
and 0.067 to 0.135 for WSIs-2 (Figure S1). "
This sentence has no proper meaning.
11. "Trained based on the precise labeled tiles, the accuracy of five biomarkers models in
the testing sets for predicting PAN-CK, P40, Desmin, P53, and Ki-67 reached 90.81% (95%
CI: 90.38%-91.23%), 88.66% (95% CI: 88.25%-89.06%), 89.16% (95% CI: 88.65%-89.95%),
83.04% (95% CI: 82.51%-83.55%), and 86.77% (95% CI: 86.35%-87.17%), respectively (Table
1)."
What are these results compared to? What did these examiners identified as correct?
12. Besides Kappa statistics, intraclass correlation should be initiated, as well.
13. Real and virtual IHC are not proper terms for an article!
14. p53 results: Kappa=0.379
This is not a good results, however,  in the discussion, the model is mentioned to be amazing.
It is not.
15. The interpratation of the Kappa statistics is not mentioned in the article at all.
16. It is not clear at all how T stage was evaluated.
17. It is mentioned nowhere what were the histological subtypes of the investigated tumors. That
is a major problem.
18. The list of abbreviations is completely unprofessional.
19. Why does the reference list has  a differing lettering? Unprofessional again.
20. Legends are short and unprofessional.
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's report:
Major issues:
1. "Five biomarker prediction models (P40, Pan-CK, Desmin, P53, Ki-67) were developed
following WHO's fifth tumor classification, with quantification of Ki-67 proliferation index."
Those are immunohistochemistry reactions in the bracket, why are they phrased as biomarker
prediction models?
2. Why are these reactions chosen? These are completely different IHC reaction, with totally
different interpretation.
3. FigS1A is not necessary.
4. The cover letter provided for the revision is not professional, and does not include responeses,
even though that is its title.
5. The English grammar of the article needs major revisions.
6. "With widespread adoption, the labor intensity, time consuming, the shortage of pathologists
and tissue costs present inherent limitations."
What does this sentence mean? The reactions will always be delivered by technicians, therefore,
it would never be replaced by AI.
The interpretation of IHC slides takes way less time every day then it would be beneficial to
use AI for that.
And if AI is initiated for the interpretation, and AI makes a mistake, who takes the responsibility?
Therefore, AI would never be able to replace a pathologist. So instead of FOMO, I recommend
to do science that actually helps diagnostics in the long run.
7. When an IHC reaction is initiated, there has to be positive control to see whether the reaction
is working at all or not. This was not included in this study, therefore, we are not even certain
whether these reactions worked properly, or not...
And if there were positive control on all slides, how would AI differentiate it from true positivity?
8. The KI-67 interpretation is very superficial and not professional at all.
9. "The fifth edition of the WHO recommends routinely investigating these five fundamental
biomarkers in GI cancer for pathology clinical practice: Pan-CK, P40, Desmin, P53, Ki-67."
These markers are used for completely different tumours. What does WHO recommendation
mean?!
10. "Using the mutual information values of the 1st to 2nd slides (0.658 for WSIs-1; 0.716 for
WSIs-2) as a reference, the mutual information values of the 1st to 3rd, 1st to 4th... 1st to 10th in
succession show a negligible decrease, with the decline ranging from 0.054 to 0.132 for WSIs-1
and 0.067 to 0.135 for WSIs-2 (Figure S1). "
This sentence has no proper meaning.
11. "Trained based on the precise labeled tiles, the accuracy of five biomarkers models in
the testing sets for predicting PAN-CK, P40, Desmin, P53, and Ki-67 reached 90.81% (95%
CI: 90.38%-91.23%), 88.66% (95% CI: 88.25%-89.06%), 89.16% (95% CI: 88.65%-89.95%),
83.04% (95% CI: 82.51%-83.55%), and 86.77% (95% CI: 86.35%-87.17%), respectively (Table
1)."
What are these results compared to? What did these examiners identified as correct?
12. Besides Kappa statistics, intraclass correlation should be initiated, as well.
13. Real and virtual IHC are not proper terms for an article!
14. p53 results: Kappa=0.379
This is not a good results, however,  in the discussion, the model is mentioned to be amazing.
It is not.
15. The interpratation of the Kappa statistics is not mentioned in the article at all.
16. It is not clear at all how T stage was evaluated.
17. It is mentioned nowhere what were the histological subtypes of the investigated tumors. That
is a major problem.
18. The list of abbreviations is completely unprofessional.
19. Why does the reference list has  a differing lettering? Unprofessional again.
20. Legends are short and unprofessional.
assistant