Title: The Development of a Multimodal Prediction Model Based on CT and MRI for the
Prognosis of Pancreatic Cancer
Version: 0 Date: 01 May 2025
Reviewer's report:
This study developed and validated a hybrid radiomics model to predict the overall survival in
pancreatic cancer patients and identify risk factors that affect patient prognosis. A major revision
was recommended before re-consideration.
1)The data span from 2013 to 2023, but the manuscript does not address potential temporal
biases, evolution of imaging protocols, or treatment regimens.
2)Many patients did not undergo surgery, and important prognostic factors like microvascular
invasion, perineural invasion, and margin status were missing, which limits model robustness.
3)In the introduction part, the authors should emphasize the dire need of diagnosis
pancreatic cancer at an early stage and following references could be added: doi: 10.4251/
wjgo.v16.i4.1256; doi: 10.2147/IJN.S335588.
4)The decision to retain 80% variance in PCA is common, but the specific selection of 15–18
components lacks detailed rationale, especially when dealing with non-linear RSF models.
5)Although the model predicts survival, it doesn’t classify patients into low-, intermediate-, or
high-risk groups, which is essential for clinical utility.
6)Radiomics-based approaches are widely utilized in tumor staging, early diagnosis,
differentiation, prognosis prediction, and treatment evaluation. I recommend that the authors
emphasize the clinical significance of radiomics in the revised manuscript. The following
references may be considered to support this context: DOI: 10.1007/s11596-022-2535-2; doi:
10.1007/s00261-020-02506-6; doi: 10.3389/fonc.2020.01618.
7)Although two physicians annotated the ROIs, the study does not quantify inter-rater agreement
(e.g., Dice coefficient), which is critical in radiomics.
8)Although PCA and LASSO were applied for feature reduction, the final RSF model is trained
on relatively high-dimensional data (many radiomics + clinical features) with only 190 training
samples, which still poses a risk of overfitting—especially with machine learning methods like
RSF.
9)The study compares multiple models (unimodal, bimodal, hybrid, COX, RSF) without clearly
adjusting for multiple testing, increasing the risk of Type I error.
10)Key performance metrics (e.g., C-index, Brier score) are reported without 95% confidence
intervals, which are essential to assess statistical uncertainty.
11)No power analysis or rationale is provided to justify that the sample size (n=272) is sufficient
given the number of features used.
Title: The Development of a Multimodal Prediction Model Based on CT and MRI for the
Prognosis of Pancreatic Cancer
Version: 0 Date: 01 May 2025
Reviewer's report:
This study developed and validated a hybrid radiomics model to predict the overall survival in
pancreatic cancer patients and identify risk factors that affect patient prognosis. A major revision
was recommended before re-consideration.
1)The data span from 2013 to 2023, but the manuscript does not address potential temporal
biases, evolution of imaging protocols, or treatment regimens.
2)Many patients did not undergo surgery, and important prognostic factors like microvascular
invasion, perineural invasion, and margin status were missing, which limits model robustness.
3)In the introduction part, the authors should emphasize the dire need of diagnosis
pancreatic cancer at an early stage and following references could be added: doi: 10.4251/
wjgo.v16.i4.1256; doi: 10.2147/IJN.S335588.
4)The decision to retain 80% variance in PCA is common, but the specific selection of 15–18
components lacks detailed rationale, especially when dealing with non-linear RSF models.
5)Although the model predicts survival, it doesn’t classify patients into low-, intermediate-, or
high-risk groups, which is essential for clinical utility.
6)Radiomics-based approaches are widely utilized in tumor staging, early diagnosis,
differentiation, prognosis prediction, and treatment evaluation. I recommend that the authors
emphasize the clinical significance of radiomics in the revised manuscript. The following
references may be considered to support this context: DOI: 10.1007/s11596-022-2535-2; doi:
10.1007/s00261-020-02506-6; doi: 10.3389/fonc.2020.01618.
7)Although two physicians annotated the ROIs, the study does not quantify inter-rater agreement
(e.g., Dice coefficient), which is critical in radiomics.
8)Although PCA and LASSO were applied for feature reduction, the final RSF model is trained
on relatively high-dimensional data (many radiomics + clinical features) with only 190 training
samples, which still poses a risk of overfitting—especially with machine learning methods like
RSF.
9)The study compares multiple models (unimodal, bimodal, hybrid, COX, RSF) without clearly
adjusting for multiple testing, increasing the risk of Type I error.
10)Key performance metrics (e.g., C-index, Brier score) are reported without 95% confidence
intervals, which are essential to assess statistical uncertainty.
11)No power analysis or rationale is provided to justify that the sample size (n=272) is sufficient
given the number of features used.
Please review this paper.
user
Title: The Development of a Multimodal Prediction Model Based on CT and MRI for the
Prognosis of Pancreatic Cancer
Version: 0 Date: 01 May 2025
Reviewer's report:
This study developed and validated a hybrid radiomics model to predict the overall survival in
pancreatic cancer patients and identify risk factors that affect patient prognosis. A major revision
was recommended before re-consideration.
1)The data span from 2013 to 2023, but the manuscript does not address potential temporal
biases, evolution of imaging protocols, or treatment regimens.
2)Many patients did not undergo surgery, and important prognostic factors like microvascular
invasion, perineural invasion, and margin status were missing, which limits model robustness.
3)In the introduction part, the authors should emphasize the dire need of diagnosis
pancreatic cancer at an early stage and following references could be added: doi: 10.4251/
wjgo.v16.i4.1256; doi: 10.2147/IJN.S335588.
4)The decision to retain 80% variance in PCA is common, but the specific selection of 15–18
components lacks detailed rationale, especially when dealing with non-linear RSF models.
5)Although the model predicts survival, it doesn’t classify patients into low-, intermediate-, or
high-risk groups, which is essential for clinical utility.
6)Radiomics-based approaches are widely utilized in tumor staging, early diagnosis,
differentiation, prognosis prediction, and treatment evaluation. I recommend that the authors
emphasize the clinical significance of radiomics in the revised manuscript. The following
references may be considered to support this context: DOI: 10.1007/s11596-022-2535-2; doi:
10.1007/s00261-020-02506-6; doi: 10.3389/fonc.2020.01618.
7)Although two physicians annotated the ROIs, the study does not quantify inter-rater agreement
(e.g., Dice coefficient), which is critical in radiomics.
8)Although PCA and LASSO were applied for feature reduction, the final RSF model is trained
on relatively high-dimensional data (many radiomics + clinical features) with only 190 training
samples, which still poses a risk of overfitting—especially with machine learning methods like
RSF.
9)The study compares multiple models (unimodal, bimodal, hybrid, COX, RSF) without clearly
adjusting for multiple testing, increasing the risk of Type I error.
10)Key performance metrics (e.g., C-index, Brier score) are reported without 95% confidence
intervals, which are essential to assess statistical uncertainty.
11)No power analysis or rationale is provided to justify that the sample size (n=272) is sufficient
given the number of features used.
assistant