Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 03 Mar 2025
Reviewer's report:
The authors developed a deep learning model capable of predicting IHC staining patterns
from H&E WSIs. A total of 134 WSIs, both H&E and IHC, were used for training. Labels
were annotated automatically, with pathologist verification. The model was tested, showing a
relatively high AUC. Additionally, the authors conducted a MRMC study to further assess the
clinical applicability of the deep learning models, suggesting high concordance and an overall
satisfactory rate. My comments are as follows:
1. Materials and Methods – WSIs Preparation:  The dataset includes 134 WSIs from 73 patients.
Does this mean that not all patients had the full set of five essential IHC stains for gastrointestinal
tumor diagnostics? The authors should provide a breakdown of which stains were available for
which cases.
2. The manuscript should specify the scanner model name (including manufacturer and country).
Additionally, why were certain IHC slides scanned with one scanner and others with a different
one? The lack of mention of tile normalization process in the manuscript could introduce
standardization issues.
3. The quantification process for Ki-67 is unclear. The manuscript suggests that the Ki-67 index
is calculated by first predicting the invasive cancer area with models P40 and PCK, and then
predicting Ki-67 positivity to obtain the final ratio. However, P40 and PCK can also be positive
for benign cells. How was the invasive cancer area accurately predicted by the AI to ensure
correct Ki-67 quantification?
4. In the section Material and Methods: MRMC Study for Clinical Application, the third line
states: “xx has more than 10 years”. This sentence needs completion.
5. The comparison of satisfaction levels between real IHC and virtual IHC appears subjective.
It is unclear how this data contributes to the study's scientific validity.
6. Material and methods - Automatic annotation of target regions: The first sentence in this
section is phrased in an unconventional manner. Consider rewording for clarity.
7. The version of the VIA online software and SPSS (SPSS 27) should be specified, along with
the manufacturer and country.
8. Some abbreviations should be spelled out on first mention (e.g., AI, WURH, RHWU).
9. Results – Clinical Validation of MRMC Study: "Tree" should be corrected to "Three".
Title:  Development and clinical validation of deep learning-based Immunohistochemistry
prediction models for subtyping and staging of gastrointestinal cancers
Version: 4 Date: 03 Mar 2025
Reviewer's report:
The authors developed a deep learning model capable of predicting IHC staining patterns
from H&E WSIs. A total of 134 WSIs, both H&E and IHC, were used for training. Labels
were annotated automatically, with pathologist verification. The model was tested, showing a
relatively high AUC. Additionally, the authors conducted a MRMC study to further assess the
clinical applicability of the deep learning models, suggesting high concordance and an overall
satisfactory rate. My comments are as follows:
1. Materials and Methods – WSIs Preparation:  The dataset includes 134 WSIs from 73 patients.
Does this mean that not all patients had the full set of five essential IHC stains for gastrointestinal
tumor diagnostics? The authors should provide a breakdown of which stains were available for
which cases.
2. The manuscript should specify the scanner model name (including manufacturer and country).
Additionally, why were certain IHC slides scanned with one scanner and others with a different
one? The lack of mention of tile normalization process in the manuscript could introduce
standardization issues.
3. The quantification process for Ki-67 is unclear. The manuscript suggests that the Ki-67 index
is calculated by first predicting the invasive cancer area with models P40 and PCK, and then
predicting Ki-67 positivity to obtain the final ratio. However, P40 and PCK can also be positive
for benign cells. How was the invasive cancer area accurately predicted by the AI to ensure
correct Ki-67 quantification?
4. In the section Material and Methods: MRMC Study for Clinical Application, the third line
states: “xx has more than 10 years”. This sentence needs completion.
5. The comparison of satisfaction levels between real IHC and virtual IHC appears subjective.
It is unclear how this data contributes to the study's scientific validity.
6. Material and methods - Automatic annotation of target regions: The first sentence in this
section is phrased in an unconventional manner. Consider rewording for clarity.
7. The version of the VIA online software and SPSS (SPSS 27) should be specified, along with
the manufacturer and country.
8. Some abbreviations should be spelled out on first mention (e.g., AI, WURH, RHWU).
9. Results – Clinical Validation of MRMC Study: "Tree" should be corrected to "Three".
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: 03 Mar 2025
Reviewer's report:
The authors developed a deep learning model capable of predicting IHC staining patterns
from H&E WSIs. A total of 134 WSIs, both H&E and IHC, were used for training. Labels
were annotated automatically, with pathologist verification. The model was tested, showing a
relatively high AUC. Additionally, the authors conducted a MRMC study to further assess the
clinical applicability of the deep learning models, suggesting high concordance and an overall
satisfactory rate. My comments are as follows:
1. Materials and Methods – WSIs Preparation:  The dataset includes 134 WSIs from 73 patients.
Does this mean that not all patients had the full set of five essential IHC stains for gastrointestinal
tumor diagnostics? The authors should provide a breakdown of which stains were available for
which cases.
2. The manuscript should specify the scanner model name (including manufacturer and country).
Additionally, why were certain IHC slides scanned with one scanner and others with a different
one? The lack of mention of tile normalization process in the manuscript could introduce
standardization issues.
3. The quantification process for Ki-67 is unclear. The manuscript suggests that the Ki-67 index
is calculated by first predicting the invasive cancer area with models P40 and PCK, and then
predicting Ki-67 positivity to obtain the final ratio. However, P40 and PCK can also be positive
for benign cells. How was the invasive cancer area accurately predicted by the AI to ensure
correct Ki-67 quantification?
4. In the section Material and Methods: MRMC Study for Clinical Application, the third line
states: “xx has more than 10 years”. This sentence needs completion.
5. The comparison of satisfaction levels between real IHC and virtual IHC appears subjective.
It is unclear how this data contributes to the study's scientific validity.
6. Material and methods - Automatic annotation of target regions: The first sentence in this
section is phrased in an unconventional manner. Consider rewording for clarity.
7. The version of the VIA online software and SPSS (SPSS 27) should be specified, along with
the manufacturer and country.
8. Some abbreviations should be spelled out on first mention (e.g., AI, WURH, RHWU).
9. Results – Clinical Validation of MRMC Study: "Tree" should be corrected to "Three".
assistant