Title: Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the
pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma
Version: 0 Date: 08 Dec 2023
Reviewer's report:
The paper requires major revisions.
1. Could you provide more details on the criteria used to determine the needs for postoperative
radiotherapy based on the final histopathology results according to the Peter/Sedlis standard? It
would be helpful to understand how these criteria were applied in the study.
2. In the Methods section, you mentioned using a 4:1 ratio to divide the dataset into two cohorts.
What criteria were considered when splitting the data into these cohorts, and how was this ratio
determined?
3. Please clarify the rationale behind choosing the specific MRI sequences (T2WI, T2WI with
fat suppression, and contrast-enhanced T1WI) for obtaining MR images in your study. Were
there specific advantages or characteristics of these sequences that influenced your choice?
4. Could you elaborate on the decision-making process for postoperative radiotherapy options
based on the final histopathology results? How did the Peter/Sedlis standard guide the selection
of these options?
5. Provide more details on the features extracted using the least absolute shrinkage and selection
operator (LASSO) regression. What clinical and radiomic features were considered, and how
did the LASSO regression contribute to the extraction process?
6. The AUC values for DLR_Sig in the training and test cohorts are provided, but could you
discuss the clinical significance of these values? How do these results impact the potential
clinical application of DLR_Sig?
7. Please elaborate on the significance of the p-values obtained from the Hosmer-Lemeshow test
for the training and test cohorts. What do these values indicate about the model's calibration?
8. In the DeLong test, you mentioned that the predictive effectiveness of DLR_Sig was
significantly better than Clinic_Sig. Could you provide more insights into the specific
improvements and their clinical implications?
9. The gradual improvement trend among Rad_Sig, DTL_Sig, and DLR_Sig is mentioned.
Could you discuss the possible reasons behind this trend, even though no significant statistical
difference was observed?
10. In the conclusion, you mentioned that DLR_Sig shows promise as a non-invasive predictor
for adjuvant radiotherapy strategies. Can you elaborate on the potential clinical impact of
implementing DLR_Sig in the decision-making process for adjuvant radiotherapy?
11. Provide more context on the choice of the five pre-trained Convolutional Neural Network
(CNN) models for the Deep Transformer Learning Signature (DTL_Sig). What criteria were
used to select these models, and how did they contribute to the study?
12. In the introduction, you mentioned that the study aimed to minimize adverse events
associated with cancer treatment. Could you elaborate on how the developed biomarker
contributes to minimizing these adverse events, especially those related to surgery combined
with radiotherapy? Add suitable references to the site with [PMID: 37238175, PMID: 37701174]
13. Clarify the role of the Peter and Sedlis standards in determining the postoperative
radiotherapy options. How did these standards guide the decision-making process for each
patient?
14. Provide more details on the patient characteristics considered during the random division into
training and validation cohorts. Were there specific demographic or clinical factors considered
to ensure representative cohorts?
15. Could you provide more information on the decision points in the flow chart? Specifically,
what criteria were used to determine the inclusion or exclusion of patients at each step?
16. Discuss the rationale behind choosing the specific MRI scanners (3.0T and 1.5T) and their
respective proportions in the study and Radiomics features as a metric. Were there specific
considerations for using one scanner over the other? Add suitable references to the site with
[PMID: 37509228, PMID: 37993801].
17. Explain the rationale for choosing the 0.5 to 99.5 percentiles range for truncating pixel values
in medical images. How does this range selection contribute to the data preprocessing steps?
18. Provide more insights into the rationale for choosing the specific pre-trained CNN models
(resnet50, resnet101, inception_v3, densenet121) for the study. How did these models contribute
to the extraction of features in the radiomics procedure?