Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 09 Dec 2024
Reviewer's report:
Commments 1:  I believe it is essential to verify whether there is any overlap of patients across
the test, validation, and training datasets.
Commments 2: Sensitivity, as a true positive measure, does not account for instances where a
lesion is completely missed. Since ensuring no lesion is overlooked is critical for developing a
model fit for the intended purpose, model performance evaluation should also include metrics
such as NPV (Negative Predictive Value) to present the model's effective detection probability.
Commments 3: It would be helpful to provide justification for setting the IoU threshold at 0.125.
Commments 4: Comparing the classification performance with other state-of-the-art (SOTA)
models would strengthen the study. Alternatively, presenting metrics that align with and validate
the reported performance indicators would be valuable.  The sensitivity values provided—0.815,
0.738, and 0.883 for lesions # 6 mm, 6 to 10 mm, and # 10 mm, respectively—are difficult to
interpret in isolation. Further context or explanation regarding these metrics is needed.
Title: Development and validation of computer-aided detection for colorectal neoplasms using
deep learning incorporated with computed tomography colonography
Version: 2 Date: 09 Dec 2024
Reviewer's report:
Commments 1:  I believe it is essential to verify whether there is any overlap of patients across
the test, validation, and training datasets.
Commments 2: Sensitivity, as a true positive measure, does not account for instances where a
lesion is completely missed. Since ensuring no lesion is overlooked is critical for developing a
model fit for the intended purpose, model performance evaluation should also include metrics
such as NPV (Negative Predictive Value) to present the model's effective detection probability.
Commments 3: It would be helpful to provide justification for setting the IoU threshold at 0.125.
Commments 4: Comparing the classification performance with other state-of-the-art (SOTA)
models would strengthen the study. Alternatively, presenting metrics that align with and validate
the reported performance indicators would be valuable.  The sensitivity values provided—0.815,
0.738, and 0.883 for lesions # 6 mm, 6 to 10 mm, and # 10 mm, respectively—are difficult to
interpret in isolation. Further context or explanation regarding these metrics is needed.
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: 09 Dec 2024
Reviewer's report:
Commments 1:  I believe it is essential to verify whether there is any overlap of patients across
the test, validation, and training datasets.
Commments 2: Sensitivity, as a true positive measure, does not account for instances where a
lesion is completely missed. Since ensuring no lesion is overlooked is critical for developing a
model fit for the intended purpose, model performance evaluation should also include metrics
such as NPV (Negative Predictive Value) to present the model's effective detection probability.
Commments 3: It would be helpful to provide justification for setting the IoU threshold at 0.125.
Commments 4: Comparing the classification performance with other state-of-the-art (SOTA)
models would strengthen the study. Alternatively, presenting metrics that align with and validate
the reported performance indicators would be valuable.  The sensitivity values provided—0.815,
0.738, and 0.883 for lesions # 6 mm, 6 to 10 mm, and # 10 mm, respectively—are difficult to
interpret in isolation. Further context or explanation regarding these metrics is needed.
assistant