Title: The Development of a Multimodal Prediction Model Based on CT and MRI for the
Prognosis of Pancreatic Cancer
Version: 0 Date: 30 May 2025
Reviewer's report:
This is a rigorous retrospective study aiming to develop a multimodal radiomics model based
on clinical data, contrast-enhanced CT, and MRI using the RSF algorithm to predict prognosis
in pancreatic cancer patients, providing a potentially valuable tool for individualized treatment
decisions. The methodology is generally sound, the writing is clear, and the visualizations
are comprehensive. However, several clarifications or improvements are needed before the
manuscript can be considered for publication:
1. Image Preprocessing:
Why was resampling to 3×3×3 mm³ chosen instead of the commonly used 1×1×1 mm³?
Why was signal intensity normalization not performed for the images?
2. Clinical Feature Dimensionality Reduction:
Why was Lasso used for clinical feature dimensionality reduction instead of the commonly used
univariate/multivariate logistic regression?
3. Inappropriate Terminology:
In "The Development and Evaluation of Models" section, the terms "imaging histologic features"
and "genomics model" are misused. The study does not involve histology or genomics research.
4. Feature Annotations:
Please annotate the meanings of features such as ct_pc1, ct_pc2, ct_pc15, and mri_pc2 to aid
reader understanding.
5. Discussion Section:
Which machine learning algorithms are commonly used for survival data?
Why were RSF and Cox chosen for comparison, and have there been prior studies comparing
similar algorithms?
Title: The Development of a Multimodal Prediction Model Based on CT and MRI for the
Prognosis of Pancreatic Cancer
Version: 0 Date: 30 May 2025
Reviewer's report:
This is a rigorous retrospective study aiming to develop a multimodal radiomics model based
on clinical data, contrast-enhanced CT, and MRI using the RSF algorithm to predict prognosis
in pancreatic cancer patients, providing a potentially valuable tool for individualized treatment
decisions. The methodology is generally sound, the writing is clear, and the visualizations
are comprehensive. However, several clarifications or improvements are needed before the
manuscript can be considered for publication:
1. Image Preprocessing:
Why was resampling to 3×3×3 mm³ chosen instead of the commonly used 1×1×1 mm³?
Why was signal intensity normalization not performed for the images?
2. Clinical Feature Dimensionality Reduction:
Why was Lasso used for clinical feature dimensionality reduction instead of the commonly used
univariate/multivariate logistic regression?
3. Inappropriate Terminology:
In "The Development and Evaluation of Models" section, the terms "imaging histologic features"
and "genomics model" are misused. The study does not involve histology or genomics research.
4. Feature Annotations:
Please annotate the meanings of features such as ct_pc1, ct_pc2, ct_pc15, and mri_pc2 to aid
reader understanding.
5. Discussion Section:
Which machine learning algorithms are commonly used for survival data?
Why were RSF and Cox chosen for comparison, and have there been prior studies comparing
similar algorithms?
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: 30 May 2025
Reviewer's report:
This is a rigorous retrospective study aiming to develop a multimodal radiomics model based
on clinical data, contrast-enhanced CT, and MRI using the RSF algorithm to predict prognosis
in pancreatic cancer patients, providing a potentially valuable tool for individualized treatment
decisions. The methodology is generally sound, the writing is clear, and the visualizations
are comprehensive. However, several clarifications or improvements are needed before the
manuscript can be considered for publication:
1. Image Preprocessing:
Why was resampling to 3×3×3 mm³ chosen instead of the commonly used 1×1×1 mm³?
Why was signal intensity normalization not performed for the images?
2. Clinical Feature Dimensionality Reduction:
Why was Lasso used for clinical feature dimensionality reduction instead of the commonly used
univariate/multivariate logistic regression?
3. Inappropriate Terminology:
In "The Development and Evaluation of Models" section, the terms "imaging histologic features"
and "genomics model" are misused. The study does not involve histology or genomics research.
4. Feature Annotations:
Please annotate the meanings of features such as ct_pc1, ct_pc2, ct_pc15, and mri_pc2 to aid
reader understanding.
5. Discussion Section:
Which machine learning algorithms are commonly used for survival data?
Why were RSF and Cox chosen for comparison, and have there been prior studies comparing
similar algorithms?
assistant