Review for 10.1186/s12876-025-04045-0 (2025)

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 study is very interesting. The authors wrote the manuscript in simple clear language.
The inclusion of pathologists in the study with an online questionnaire after completing the
trail, made the study reliable and more applicable in daily routine practice. Tables of detailed
pathologic data and figures in supplementary file added valuable information. However, minor
inquiries are needed to be clarified:
Title:
• I can see two titles, first is:  Development and clinical validation of deep learning based
virtual Immunohistochemistry staining models in gastrointestinal cancers  AND:  Automatic
development and clinical validation of virtual immunohistochemistry staining models in
gastrointestinal cancers. Please choose one.
• I recommend adding the name of immunohistochemical markers tested in title or at least adding
the aim of IHC markers chosen (staging and subtyping of GIT tumors)
Abstract: simple and clear
Introduction: Please mention more details about the other studies that assessed same markers
and the challenges they faced.
Methodology:
• Please clarify why those 5 markers were chosen in your study. Ki67 and P53 are commonly
used in tumors all over the body. But what is the role of PanCK, P40, and desmin? I have noticed
that P40 is used in esophageal squamous cell carcinomas. Does it aim to confirm the squamous
nature of tumor? or it aimed at highlighting the tumor cells for further assessment by Ki67 and
P53?. Regarding the desmin and PanCK, I expect they are used in TNM staging. Please clarify
this in methodology to avoid readers’ confusion.
• Please clarify, how areas of necrosis (which are known to show nonspecific staining in IHC)
and nearby normal tissue (stain panCK positive and P40 positive) were excluded? How stromal
cells were not included in P53 and Ki67 immunostaining assessment? How blood vessels walls
(normally stains positive by desmin) were removed in desmin stain assessment?
• Please clarify the criteria of reporting P53 results ( P53 overexpression versus normal) in your
study.
• I noticed that pathologists must revise the areas of interest in tumors before assessment by AI
models. In addition, I understand from your study that to calculate Ki67 labeling index, the same
slide must be stained by pan CK or P40 to allow the machine to correctly choose tumor area.
Do you think this is cost effective in clinical practice?
Results:
• The authors wrote the results in clear ordered text with representative figures. However, I
noticed that some non neoplastic conditions as gastritis and polyps were included in their results.
This is not mentioned in their methodology or aim. Please clarify why those cases were included?
• Regarding P53 expression, it was reported to be overexpressed in all cancers included. In
literature, some (20-30%) of gastric and esophageal cancers are reported to be P53 wild/normal
pattern  . Why all included cases showed P53 overexpression?
Discussion:
• Very concise, no clear data regarding individual markers. As pathologist, I need to know the
detailed results and challenges faced by authors in different markers assessment. If P40 was used
to confirm the squamous nature of esophageal cancers,  How they avoided the positive staining
in normal esophageal mucosa? P53 and Ki67 labeling, How the model avoided the positive
nuclear staining in stromal cells and inflammatory infiltrate within tumor area?
• among results, The least sensitivity was seen in P53 assessment. I think authors must explain
this in their discussion, mentioning the causes and challenges of the assessment of this marker.
What is their recommendation to improve this?. The explanation written (due to weaker
correlation between protein expression and cellular morphology) is not convincing .
• Please add limitations of your study and recommendation for future research.