Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 22 Dec 2024
Reviewer's report:
Thank you for your invitation to review the article titled: “Development and validation
of computer-aided detection using deep learning incorporated with computed tomography
colonography.” Here are my suggestions to improve the article.
While the authors aimed to develop deep learning algorithms using CT colonography, the goal
isn't clearly stated in the title and the beginning of the manuscript. They mention “neoplastic
lesions in CT colonography images,” but it's not clear if this includes all types of metastatic
lesions as well. Given that the data for training comes from colorectal cancer patients, it would
be helpful if the title and manuscript clearly indicated this focus.
Additionally, the manuscript lacks clear inclusion and exclusion criteria, along with information
about the tumor stages, CT properties, and the study duration. The details about the annotation
process, training, and how the data was divided into training and validation sets are also too
vague and general.
Overall, the manuscript suffers from some redundancy and vague language, making it difficult
to extract the necessary details. I recommend rewriting the methodology section to be clearer
and more concise.
At this stage, I believe the manuscript isn’t quite ready for publication and would benefit from
significant revisions before moving forward.
Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 22 Dec 2024
Reviewer's report:
Thank you for your invitation to review the article titled: “Development and validation
of computer-aided detection using deep learning incorporated with computed tomography
colonography.” Here are my suggestions to improve the article.
While the authors aimed to develop deep learning algorithms using CT colonography, the goal
isn't clearly stated in the title and the beginning of the manuscript. They mention “neoplastic
lesions in CT colonography images,” but it's not clear if this includes all types of metastatic
lesions as well. Given that the data for training comes from colorectal cancer patients, it would
be helpful if the title and manuscript clearly indicated this focus.
Additionally, the manuscript lacks clear inclusion and exclusion criteria, along with information
about the tumor stages, CT properties, and the study duration. The details about the annotation
process, training, and how the data was divided into training and validation sets are also too
vague and general.
Overall, the manuscript suffers from some redundancy and vague language, making it difficult
to extract the necessary details. I recommend rewriting the methodology section to be clearer
and more concise.
At this stage, I believe the manuscript isn’t quite ready for publication and would benefit from
significant revisions before moving forward.
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: 22 Dec 2024
Reviewer's report:
Thank you for your invitation to review the article titled: “Development and validation
of computer-aided detection using deep learning incorporated with computed tomography
colonography.” Here are my suggestions to improve the article.
While the authors aimed to develop deep learning algorithms using CT colonography, the goal
isn't clearly stated in the title and the beginning of the manuscript. They mention “neoplastic
lesions in CT colonography images,” but it's not clear if this includes all types of metastatic
lesions as well. Given that the data for training comes from colorectal cancer patients, it would
be helpful if the title and manuscript clearly indicated this focus.
Additionally, the manuscript lacks clear inclusion and exclusion criteria, along with information
about the tumor stages, CT properties, and the study duration. The details about the annotation
process, training, and how the data was divided into training and validation sets are also too
vague and general.
Overall, the manuscript suffers from some redundancy and vague language, making it difficult
to extract the necessary details. I recommend rewriting the methodology section to be clearer
and more concise.
At this stage, I believe the manuscript isn’t quite ready for publication and would benefit from
significant revisions before moving forward.
assistant