Title: Development and Validation of an Artificial Intelligence Model for Predicting De Novo
Distant Bone Metastasis in Breast Cancer: A Dual-Center Study
Version: 1 Date: 30 Jun 2024
Reviewer's report:
The study titled "Development and Validation of an Artificial Intelligence Model for Predicting
De Novo Distant Bone Metastasis in Breast Cancer: A Dual-Center Study" aims to create
an accurate model for predicting distant bone metastasis in breast cancer using readily
available clinical data and advanced machine learning algorithms. Data from 239 patients
from two medical centers were analyzed, focusing on clinical blood biomarkers. The model
was constructed and validated using the LightGBM algorithm. The model demonstrated high
accuracy with AUC values of 0.945, 0.892, and 0.908 in the training, internal testing, and
external validation cohorts, respectively. Key features influencing the model included CEA,
creatine kinase, albumin/globulin ratio, apolipoprotein B, and CA153. The study concludes that
the AI model shows significant promise in early identification of bone metastasis in breast
cancer, potentially improving clinical decision-making and patient outcomes.
Evaluation:
This study is commendable for the following reasons:
Innovative Approach: Utilizing machine learning, particularly the LightGBM algorithm,
represents a modern and efficient predictive modeling method, offering improvements in
flexibility and accuracy over traditional techniques.
Clinical Relevance: By focusing on easily obtainable clinical blood biomarkers, the model
enhances applicability in real-world settings, potentially aiding clinicians in early diagnosis and
treatment planning.
Comprehensive Validation: Including both internal and external validation cohorts increases the
credibility and generalizability of the study's findings.
Overall, this study makes a valuable contribution to the field of oncology, demonstrating the
potential of AI-driven models in improving early detection and management of breast cancer
metastasis. The approach and findings may pave the way for the widespread adoption of machine
learning tools in clinical settings, ultimately enhancing patient care and outcomes.
However, the following issues need to be addressed:
1.In the abstract, "LASSO" and "AUC" should be spelled out in full when they first appear.
2.In the background section, "CT", "PET-CT", and "ML" should be removed if they only appear
once in the entire text.
3.In the "Materials and Methods" section under "Feature Extraction and Selection", specify the
exact indicators for liver function, kidney function, lipid profile, and cardiovascular function.
4.In the "Materials and Methods" section under "Development and Validation of Models", delete
"PPV" and "NPV".
5.In the "Results" section under "Feature Importance Analysis and Logistic Regression
Analysis", correct the term "white blood cell count" to "albumin-globulin ratio" if that is what
is meant.
6.In the "Results" section under "Feature Importance Analysis and Logistic Regression
Analysis", clarify that CK-MB, CA153, and alkaline phosphatase are positively correlated in
the multivariate analysis.
7.In the discussion, use the full name for "NCBI GEO database".
8.In the discussion, specify that "XGB" refers to "XGBoost".
9.In the discussion, use the full name for "SEER database".