Review for 10.1186/s12876-022-02573-7 (2022)

Reviewer’s report
Title: 
Tissue Microarrey: A potential cost-effective approach for mismatch
repair testing in colorectal cancer
Version: 
0 
Date: 
12 Jun 2022
Reviewer's report:
Authors investigated whether TMA MMR IHC-based evaluation and decision
making could be comparable to that of the whole slide-based evaluation. They
also investigated how efficient and accurate decisions could be made by an
automated image analysing model to distinguish normal and pathological TMA
cores. This is a quite interesting study. However, I would like the authors to
address comments listed as follows:
- Did authors consider alternative measures to 'Minimal staining intensity' maybe
by also considering the neighbourhood or spatial closeness/distance of the
stained regions in the image (e.g. considering only the ratio of the staining
region would not still avoid the involvement of impulsive noise regions in the
image)
- Why little is known about H-score ? Is it possible to formulate it?
- Specificity scores are found to be too low (slightly more than 50%), which also
reflects that the proposed model tend to classify any given image sample as
'pathological', as authors have also declared, this is not as dramatic as missing a
real 'pathological' case and mislabel it as a healthy one, yet labeling ~50% of
healthy samples as pathological will increase the burden on the pathologists.
Optimal parameter search for the proposed score(s) could have been searched.
AUC is a good measure, but Matthews correlation coefficient (MCC) could have
been also used.
- Binary classification of healthy and pathological samples could have been done
using traditional classifiers e.g. MLP, SVM, Random Forest, etc (trained on the
first dataset, and then could have been validated on the second set). And in the
end it'd be interesting to see comparison between the classifiers' efficiencies and
H-score and proposed score's accuracies. So that, there could have been a chance
to obtain a higher AUC and more balanced sensitivity/specificity. In the current
setup, I am not convicted with the power of the proposed metric. (I would not
suggest to use any deep learning model since the sample size is quite small as
also stated by the authors).
- It has been denoted that the deficiency of at least one of the MMR proteins
would be enough to determine a pathological case. However, it would be
interesting to see the frequencies of existence and absence of each protein across
the TP, TN, FP, FN samples, so that we could have an idea which protein(s)'
existence or absence would provide a higher accuracy; which protein(s)
contribute(s) to sensitivity or specificity more.. etc
Minor comments
There are some typos across the document (even in the title), even a simple
automated spell-check could have been useful (there are some other errors, e.g.
'then' was used instead of 'than'..)
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Is the study design appropriate to answer the research question (including the use
of appropriate controls), and are the conclusions supported by the evidence
presented?
Yes
Are the methods sufficiently described to allow the study to be repeated?
Yes
Is the use of statistics and treatment of uncertainties appropriate?
No
Is the presentation of the work clear?
Yes
Are the images in this manuscript (including electrophoretic gels and blots) free
from apparent manipulation?
Yes