Title: Development and validation of competitive risk model for older women with metaplastic
breast cancer
Version: 0 Date: 03 Jun 2023
Reviewer's report:
Review manuscript: Development and validation of competitive risk model for older women
with metaplastic breast cancer
Suggestion: Revision
Summary: The authors investigate a learning approach to create a prognosis model for
metaplastic breast cancer. The method is to train the Fine and Gray competing model to predict
survival risk and flexibly predict time to death after 1-year, 3-year, and 5-year. The authors
evaluate the model using statistical tests and qualitative comparisons against traditional TNM
staging methods. The successful results indicating the model's effectiveness and utility for
prognosis warrant further investigation.
The strengths of this work can be stated as the following.
- This study employs a sizable number of patients, which is appropriate for evaluating the utility
of the investigated approach.
- The rationale for the use of a competing risk model fits well with the problem setting.
- Evaluations from multiple tests measure and make the advantage of the approach convincing.
However, some aspects of this paper could be strengthened before publication, such as the
following.
- It is quite clear that this work closely follows the methodology of the work in (reference 15)
with different problem settings. Although the methodology is appropriate for the rationale of
the current problem setting, it would be better if the author could have brief statements that this
work follows the same or similar method and whether this work has aspects that are distinct
from the other work.
- As this work strives toward practicality to improve upon the existing prognosis model, the
author should clearly state the reason for focusing on the prediction of 1-year, 3-year, and 5-year
(e.g., does the time frame affect medical decision after prognosis). Fine and Gray regression
approach can already flexibly predict survival in different time frames. Why focus on these
survival times? This motivation should be explicitly stated clearer.
- There are many missing details on implementing the model training necessary to reproduce the
experiments. The aim of describing content material and method should also be for the reader
to be able to reproduce the experiments. The authors should define how they pre-process the
input variable for the Fine Gray model (e.g., which variables are binary, ordinal, categorical, and
continuous? How the variables are normalized?). The work seems tab on this in the Statistical
analysis section but fails to elaborate on the Fine Gray model inputs. Details on how the model
is trained (e.g., Newton method or iterative gradient descent) should be included. If the work
follows the exact implementation from some other work, those works should also be cited. On
this matter, the author seems to cite the work on (reference 15). However, the reference also
doesn’t describe training implementation other than training and validation split.
- Other minor issues which would make the work more solid if they are to be addressed.
• What is the motivation for splitting 70% training and 30% validation?
• Why not use cross-validation for C-index and AUC estimations?
• It seems that information on TNM staging is also a crucial part of the model. It would be very
interesting to compare the proposed model’s C-index and AUC with the model trained with only
TNM-staging variables. That way, readers can see how much the other variables improve the
prediction models.