An Explainable AI Approach for Breast Cancer Metastasis Prediction Based on Clinicopathological Data

Objective:
Breast Cancer is the most prevalent cancer and the first cause of cancer deaths among women worldwide. In 90% of the cases, mortality is related to distant metastasis. Computer-aided prognosis systems using machine learning models have been widely used to predict breast cancer metastasis. Despite that, these systems still face several challenges. First, the models are generally biased toward the majority class due to datasets unbalance. Second, their increased complexity is associated with decreased interpretability which causes clinicians to distrust their prognosis.
Methods:
To tackle these issues, we have proposed an explainable approach for predicting breast cancer metastasis using clinicopathological data. Our approach is based on cost-sensitive CatBoost classifier and utilises LIME explainer to provide patient-level explanations.
Results:
We used a public dataset of 716 breast cancer patients to assess our approach. The results demonstrate the superiority of cost-sensitive CatBoost in precision (76.5%), recall (79.5%), and f1-score (77%) over classical and boosting models. The LIME explainer was used to quantify the impact of patient and treatment characteristics on breast cancer metastasis, revealing that they have different impacts ranging from high impact like the non-use of adjuvant chemotherapy, and moderate impact including carcinoma with medullary features histological type, to low impact like oral contraception use. The code is available at
https://github.com/IkramMaouche/CS-CatBoost

Conclusion:
Our approach serves as a first step toward introducing more efficient and explainable computer-aided prognosis systems for breast cancer metastasis prediction.
Significance:
This approach could help clinicians understand the factors behind metastasis and assist them in proposing more patient-specific therapeutic decisions.