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Metastasis

Metastasis

Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...

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Summary

This summary is machine-generated.

We developed an explainable machine learning model to predict breast cancer metastasis, improving accuracy and providing interpretable insights for clinicians. This approach addresses data imbalance and enhances trust in computer-aided prognosis systems.

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Area of Science:

Oncology
Artificial Intelligence
Medical Informatics

Background:

Breast cancer is a leading cause of cancer death globally, with metastasis being a primary driver of mortality.
Current machine learning models for breast cancer metastasis prediction face challenges with data imbalance and lack of interpretability, hindering clinical trust.
Existing computer-aided prognosis systems often exhibit bias towards majority classes and suffer from reduced interpretability due to model complexity.

Purpose of the Study:

To develop an explainable and accurate machine learning approach for predicting breast cancer metastasis.
To overcome limitations of existing models, including class imbalance and poor interpretability.
To provide clinicians with patient-level explanations for metastasis predictions.

Main Methods:

Utilized a cost-sensitive CatBoost classifier for improved prediction accuracy on imbalanced datasets.
Integrated the LIME (Local Interpretable Model-agnostic Explanations) explainer for patient-level interpretability.
Evaluated the approach on a public dataset comprising 716 breast cancer patients.

Main Results:

The cost-sensitive CatBoost model demonstrated superior performance with precision (76.5%), recall (79.5%), and F1-score (77%) compared to traditional and boosting models.
LIME successfully quantified the impact of various patient and treatment characteristics on metastasis risk.
Identified key factors influencing metastasis, such as non-use of adjuvant chemotherapy (high impact), histological type (moderate impact), and oral contraceptive use (low impact).

Conclusions:

The proposed explainable approach enhances the efficiency and interpretability of computer-aided prognosis systems for breast cancer metastasis.
This methodology facilitates a better understanding of metastasis drivers, enabling more personalized therapeutic decisions for patients.
The study represents a significant advancement towards trustworthy AI in clinical oncology.