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Thank you for revising your manuscript one last time to address the comments of reviewer 4. The manuscript is now ready for publication.
Thank you for submitting a revised version of your manuscript, which has now been seen again by reviewer 1 in addition to an additional independent expert (reviewer 4), who was enlisted to replace reviewer 2. Although reviewer 1 raises only minor concerns, reviewer 4 notes some limitations of the model, for example, due to the limited number of pathological factors that were considered. We therefore require submission of a further revision of your manuscript that includes appropriate changes to address the reviewers' concerns. The resubmission should be accompanied by a point-by-point response to the various points raised and an explanation of how the manuscript has been revised to address these concerns.
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The revisions appropriately address the majority of reviewer and editorial concerns, with only minor residual issues related to figure legend completeness, reference formatting, and explicit cross-referencing of changes in the response letter. These could be corrected in the final proof stage.
no comment
1. Since 2018, a revised staging system for cervical cancer has been adopted, yet the manuscript lacks a clear description of the staging criteria, as well as the inclusion and exclusion criteria based on clinical and surgical-pathological staging.
2. The pathological factors considered in the study are limited to tumor type and stage, omitting other critical factors such as differentiation grade, lymph node metastasis, and vascular invasion. This limitation undermines the persuasiveness of the model's predictions.
3. There is no discussion of whether competing risks were accounted for in the prognostic model, which may affect the interpretation of the prediction results.
no comment
Lack of external validation results and single-center data are not convincing for model predictions
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The authors have made a commendable effort to revise the manuscript based on reviewer feedback. The scientific rationale, statistical rigor, and clinical relevance are improved.
Only a few minor editorial and structural refinements are needed before publication.
Please address all the reviewer comments, paying particular attention to the methodological issue raised by Reviewer 2.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
Report is added below.
Report is added below.
Report is added below.
This study presents the development and validation of a prognostic nomogram for cervical cancer patients under 50 years old using data from a prospective cohort in Southwest China. The study utilizes clinicopathologic and follow-up data from 1,004 patients collected between 2015 and 2019. A Cox regression model was used to identify independent prognostic factors, and model performance was evaluated through decision curve analysis, calibration curves, and AUC-ROC. The study finds that factors such as pathology, FIGO staging, treatment type, β2-microglobulin, neutrophil-lymphocyte ratio (NLR), and albumin levels significantly impact overall survival. The nomogram outperforms the FIGO staging system in predicting OS, suggesting its potential clinical utility.
Major Issues:
• While the model demonstrates strong predictive performance within the cohort, external validation with independent datasets from different regions or institutions is necessary to confirm its generalizability.
• The cohort is predominantly Han Chinese, limiting the applicability of findings to other ethnic groups or geographic regions with different healthcare access.
• Patients were selected from a single hospital, which may introduce selection bias. Differences in treatment protocols across institutions should be considered in future studies.
• The high concordance index in the training cohort compared to the validation cohort suggests potential overfitting. Further model refinement and external validation are required.
Minor Issues:
• While the manuscript is generally well-written, minor grammatical errors and complex sentence structures could be revised for improved readability.
• Some figures (e.g., ROC curves, DCA analysis) could benefit from more detailed captions explaining key takeaways.
• The manuscript states that data may be available upon reasonable request. Providing more transparency about access protocols would be beneficial for reproducibility.
• While Cox regression is appropriate, additional details on handling missing data and sensitivity analyses would enhance robustness.
• The study lacks a discussion on how the nomogram could be integrated into routine clinical practice. Providing practical recommendations for its use would improve the study’s impact.
The manuscript titled “Development and Validation of a Risk Prediction Model for Cervical Cancer Patients Under the Age of 50: A Southwest China Prospective Cohort Study” seems to attempt to construct a prognostic model using the clinical data of cervical cancer patients of a single center and visualize the constructed prediction model with nomogram. Model discrimination and calibration also were evaluated.
The following issues need to be raised for discussion:
The key to constructing a clinical prognosis model lies in the fact that the data used for model construction is representative and complete. The data selected in this article focuses on cervical cancer patients under the age of 50. Literature reports that the peak age of incidence of cervical cancer is 45-55 years old. Please explain in detail the purpose of selecting cervical cancer patients under the age of 50, as this directly affects the clinical application scope and clinical significance of the constructed model.
2. Some important data of cervical cancer are missing, like: tumor size, pathological grade, and the presence or absence of lymph node metastasis. Can you give an explanation or discussion about this?
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This manuscript is well structured. From introduction to study design, and then statistical analysis and results, the flow is clear for readers. One comment, line 95, there should be a space between “under” and “50”.
A few questions related to the design and analyses.
1. How did authors decide on the number of training and validation cohorts? 703 vs. 301 is almost a 2:1 ratio, what’s the decision process for this?
2. There were multiple Cox regression analyses perform. Did the authors test the PH assumptions? If so, what’s the method and result?
3. Line 169, what’s the citation for “previous research”?
4. In table 1, there are continuous variables, what test did the authors use to get p-value? Only Pearson chi-square were mentioned in the text.
Overall I find the conclusions from this manuscript to be very limited. First the data only came from one hospital, which is selection bias. What’s more, the model is very complicated, it went through multiple tests and validations, I can’t imagine how other hospitals can generalize this and get benefits. If this were a retrospective study design, and results of OS based on younger than usual population, would it be more valid?
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