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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No
Declaration of competing interests
Please complete a declaration of competing
interests, considering the following questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests
Reviewer Publication Consent.
 I agree for my report to be made available
under an Open Access Creative Commons CC-BY License (
http://creativecommons.org/licenses/by/4.0
) if this manuscript is accepted for
publication. Any comments that I do not wish to be included in the published
report have been included as confidential comments to the editor, which will not
be published.
If you are not happy for us to publish this report, please contact the
editorial office before completing the review.
If you wish, you can include your
name in your published report. Please note you must decide whether to include
your name at the start of the process and confirm this decision whenever
prompted. If you change your decision later, we will not be able to publish your
name.
I agree to the terms of the CC-BY 4.0 license; please 
do not
 publish my name
with my report. (default)
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
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'..)
Publons Reviewer Recognition
. Springer Nature can send verification of this
review directly to Publons (a subsidiary of Clarivate Analytics). If you would
like to take advantage of this service, please click on the “Yes” option below.
Your name, email address, title of the reviewed manuscript, name of the journal,
and date of your review submission (the “Review Data”) will then be transmitted
to Publons after the final decision on the manuscript has been made. If you have
already registered at Publons, they will notify you of the receipt of this review
and update your profile 
as per your settings and their policy. If you are not
registered with Publons, you will receive an email from them asking you to
register in order for them to be able to recognize your review on your new
profile page. Publons may use the Review Data to generate derivative metadata
for the benefit of Publons and you as a reviewer, carefully considering the
sensitivity of such information. For example, Publons may verify your record as
a reviewer by updating your profile published on its webservice if you have
registered for such service or help editors to identify candidate reviewers. Please
find the details of processing in Publons’ privacy policy
https://publons.com/about/terms
No
Declaration of competing interests
Please complete a declaration of competing
interests, considering the following questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests
Reviewer Publication Consent.
 I agree for my report to be made available
under an Open Access Creative Commons CC-BY License (
http://creativecommons.org/licenses/by/4.0
) if this manuscript is accepted for
publication. Any comments that I do not wish to be included in the published
report have been included as confidential comments to the editor, which will not
be published.
If you are not happy for us to publish this report, please contact the
editorial office before completing the review.
If you wish, you can include your
name in your published report. Please note you must decide whether to include
your name at the start of the process and confirm this decision whenever
prompted. If you change your decision later, we will not be able to publish your
name.
I agree to the terms of the CC-BY 4.0 license; please 
do not
 publish my name
with my report. (default)
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
Please review this paper.
user
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'..)
Publons Reviewer Recognition
. Springer Nature can send verification of this
review directly to Publons (a subsidiary of Clarivate Analytics). If you would
like to take advantage of this service, please click on the “Yes” option below.
Your name, email address, title of the reviewed manuscript, name of the journal,
and date of your review submission (the “Review Data”) will then be transmitted
to Publons after the final decision on the manuscript has been made. If you have
already registered at Publons, they will notify you of the receipt of this review
and update your profile 
as per your settings and their policy. If you are not
registered with Publons, you will receive an email from them asking you to
register in order for them to be able to recognize your review on your new
profile page. Publons may use the Review Data to generate derivative metadata
for the benefit of Publons and you as a reviewer, carefully considering the
sensitivity of such information. For example, Publons may verify your record as
a reviewer by updating your profile published on its webservice if you have
registered for such service or help editors to identify candidate reviewers. Please
find the details of processing in Publons’ privacy policy
https://publons.com/about/terms
No
Declaration of competing interests
Please complete a declaration of competing
interests, considering the following questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests
Reviewer Publication Consent.
 I agree for my report to be made available
under an Open Access Creative Commons CC-BY License (
http://creativecommons.org/licenses/by/4.0
) if this manuscript is accepted for
publication. Any comments that I do not wish to be included in the published
report have been included as confidential comments to the editor, which will not
be published.
If you are not happy for us to publish this report, please contact the
editorial office before completing the review.
If you wish, you can include your
name in your published report. Please note you must decide whether to include
your name at the start of the process and confirm this decision whenever
prompted. If you change your decision later, we will not be able to publish your
name.
I agree to the terms of the CC-BY 4.0 license; please 
do not
 publish my name
with my report. (default)
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
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