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's report:
In the present work, the Authors proficiently developed a deep-learning method to predict the
expression of key immunohistochemical (IHC) markers and Tumor (T) stage in gastrointestinal
(GI) cancers. The work is indeed interesting as novel AI-based tools are to be advocated to cope
with pathologists' shortage and help physicians handle routine-filling cumbersome tasks. I just
have a few minor concerns:
- Abstract. Within the Methods and the Results the Authors should clearly state that they are
focusing on GI cancers: what "WHO's fifth tumor classification" are they referring to?
- Abbreviations should be defined at their first mention in the text (i.e. "WURH" in the
Introduction).
- Methods. Could the Authors please detail what kind of cancers did they investigate (esophagus,
colon, stomach, pancreas, etc)? GI is a too generic definition.
- Results. Apart from a weaker correlation between protein expression and cellular morphology,
could also the lower worse with p53 IHC expression be linked to a lower interobserver
reproducibility of the evaluation of its staining?
- Discussion. The general development of WSI and AI in pathology in recent years should be
integrated with further references, focusing the main carachteristics of digital pathology (PMID
30307746 and PMID 37654717) along with the skepticism to widely adopt it (PMID 35785212),
and other different fields of AI application (PMID 38381582, PMID 33430240).
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's report:
In the present work, the Authors proficiently developed a deep-learning method to predict the
expression of key immunohistochemical (IHC) markers and Tumor (T) stage in gastrointestinal
(GI) cancers. The work is indeed interesting as novel AI-based tools are to be advocated to cope
with pathologists' shortage and help physicians handle routine-filling cumbersome tasks. I just
have a few minor concerns:
- Abstract. Within the Methods and the Results the Authors should clearly state that they are
focusing on GI cancers: what "WHO's fifth tumor classification" are they referring to?
- Abbreviations should be defined at their first mention in the text (i.e. "WURH" in the
Introduction).
- Methods. Could the Authors please detail what kind of cancers did they investigate (esophagus,
colon, stomach, pancreas, etc)? GI is a too generic definition.
- Results. Apart from a weaker correlation between protein expression and cellular morphology,
could also the lower worse with p53 IHC expression be linked to a lower interobserver
reproducibility of the evaluation of its staining?
- Discussion. The general development of WSI and AI in pathology in recent years should be
integrated with further references, focusing the main carachteristics of digital pathology (PMID
30307746 and PMID 37654717) along with the skepticism to widely adopt it (PMID 35785212),
and other different fields of AI application (PMID 38381582, PMID 33430240).
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's report:
In the present work, the Authors proficiently developed a deep-learning method to predict the
expression of key immunohistochemical (IHC) markers and Tumor (T) stage in gastrointestinal
(GI) cancers. The work is indeed interesting as novel AI-based tools are to be advocated to cope
with pathologists' shortage and help physicians handle routine-filling cumbersome tasks. I just
have a few minor concerns:
- Abstract. Within the Methods and the Results the Authors should clearly state that they are
focusing on GI cancers: what "WHO's fifth tumor classification" are they referring to?
- Abbreviations should be defined at their first mention in the text (i.e. "WURH" in the
Introduction).
- Methods. Could the Authors please detail what kind of cancers did they investigate (esophagus,
colon, stomach, pancreas, etc)? GI is a too generic definition.
- Results. Apart from a weaker correlation between protein expression and cellular morphology,
could also the lower worse with p53 IHC expression be linked to a lower interobserver
reproducibility of the evaluation of its staining?
- Discussion. The general development of WSI and AI in pathology in recent years should be
integrated with further references, focusing the main carachteristics of digital pathology (PMID
30307746 and PMID 37654717) along with the skepticism to widely adopt it (PMID 35785212),
and other different fields of AI application (PMID 38381582, PMID 33430240).
assistant