Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 21 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the abstract and methods mention that lesions # 6 mm were the target for this study,
it would be useful to clarify how lesion annotations were made in more detail. For instance,
how was the agreement between the radiologists who annotated the lesions ensured? Was
there a consensus process or inter-rater reliability testing for annotation? Greater clarity on the
annotation process would help assess the reliability and accuracy of the training data.
-The study defines lesions in three categories: # 6 mm, 6 to 10 mm, and # 10 mm. However,
the rationale for this specific categorization could be further justified. For example, why were
lesions > 10 mm not grouped with those # 6 mm, and how were lesions between 6-10 mm
considered clinically relevant? Some brief justification for these categories would be helpful in
understanding the impact on detection accuracy and sensitivity.
-The study explores the use of CT colonography in two positions (supine and prone) to improve
lesion detection. While the authors mention that merging two positions improved sensitivity,
more information on the statistical analysis and the impact of different positions on lesion
detection would strengthen the interpretation of these results. For instance, what was the average
improvement in sensitivity when using both positions compared to a single position? Were
certain types or sizes of lesions more effectively detected in one position versus the other?
-A major limitation of this study is the absence of an image interpretation experiment in which
a physician independently validates the lesions identified by the AI algorithm. While the AI
algorithm shows promise, comparing the AI’s performance with expert radiologists' assessments
would provide further validation and demonstrate the clinical utility of the AI system in real-
world decision-making.
-The study could also explore the cost-effectiveness and logistical feasibility of implementing
dual-position imaging and AI in a real-world clinical setting, particularly in low-resource
environments where there may be a shortage of radiologists. Economic analyses would be useful
for assessing the value of such technologies in colorectal cancer screening programs.
Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 21 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the abstract and methods mention that lesions # 6 mm were the target for this study,
it would be useful to clarify how lesion annotations were made in more detail. For instance,
how was the agreement between the radiologists who annotated the lesions ensured? Was
there a consensus process or inter-rater reliability testing for annotation? Greater clarity on the
annotation process would help assess the reliability and accuracy of the training data.
-The study defines lesions in three categories: # 6 mm, 6 to 10 mm, and # 10 mm. However,
the rationale for this specific categorization could be further justified. For example, why were
lesions > 10 mm not grouped with those # 6 mm, and how were lesions between 6-10 mm
considered clinically relevant? Some brief justification for these categories would be helpful in
understanding the impact on detection accuracy and sensitivity.
-The study explores the use of CT colonography in two positions (supine and prone) to improve
lesion detection. While the authors mention that merging two positions improved sensitivity,
more information on the statistical analysis and the impact of different positions on lesion
detection would strengthen the interpretation of these results. For instance, what was the average
improvement in sensitivity when using both positions compared to a single position? Were
certain types or sizes of lesions more effectively detected in one position versus the other?
-A major limitation of this study is the absence of an image interpretation experiment in which
a physician independently validates the lesions identified by the AI algorithm. While the AI
algorithm shows promise, comparing the AI’s performance with expert radiologists' assessments
would provide further validation and demonstrate the clinical utility of the AI system in real-
world decision-making.
-The study could also explore the cost-effectiveness and logistical feasibility of implementing
dual-position imaging and AI in a real-world clinical setting, particularly in low-resource
environments where there may be a shortage of radiologists. Economic analyses would be useful
for assessing the value of such technologies in colorectal cancer screening programs.
Please review this paper.
user
Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 21 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the abstract and methods mention that lesions # 6 mm were the target for this study,
it would be useful to clarify how lesion annotations were made in more detail. For instance,
how was the agreement between the radiologists who annotated the lesions ensured? Was
there a consensus process or inter-rater reliability testing for annotation? Greater clarity on the
annotation process would help assess the reliability and accuracy of the training data.
-The study defines lesions in three categories: # 6 mm, 6 to 10 mm, and # 10 mm. However,
the rationale for this specific categorization could be further justified. For example, why were
lesions > 10 mm not grouped with those # 6 mm, and how were lesions between 6-10 mm
considered clinically relevant? Some brief justification for these categories would be helpful in
understanding the impact on detection accuracy and sensitivity.
-The study explores the use of CT colonography in two positions (supine and prone) to improve
lesion detection. While the authors mention that merging two positions improved sensitivity,
more information on the statistical analysis and the impact of different positions on lesion
detection would strengthen the interpretation of these results. For instance, what was the average
improvement in sensitivity when using both positions compared to a single position? Were
certain types or sizes of lesions more effectively detected in one position versus the other?
-A major limitation of this study is the absence of an image interpretation experiment in which
a physician independently validates the lesions identified by the AI algorithm. While the AI
algorithm shows promise, comparing the AI’s performance with expert radiologists' assessments
would provide further validation and demonstrate the clinical utility of the AI system in real-
world decision-making.
-The study could also explore the cost-effectiveness and logistical feasibility of implementing
dual-position imaging and AI in a real-world clinical setting, particularly in low-resource
environments where there may be a shortage of radiologists. Economic analyses would be useful
for assessing the value of such technologies in colorectal cancer screening programs.
assistant