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Development and validation of a machine learning model for predicting invasive breast cancer using 26 routine
Lijuan Pan1, Wenjing Deng2, Ziwei Zhao3
1Department of Laboratory Medicine, The Affiliated Dazu's Hospital of Chongqing Medical University, Chongqing, China.
Background:
Invasive breast cancer (IBC) is the most prevalent malignant tumor in women globally and a leading cause of female mortality, with increasing incidence and death rates. Recent advancements in machine learning (ML) have shown significant potential in IBC prediction. This study aimed to assess different ML strategies to develop an optimal model for predicting IBC based on routine clinical examination indicators.
Methods:
We collected routine blood parameters, serum tumor marker indicators, and age data from 1,175 IBC patients at the Affiliated Dazu's Hospital of Chongqing Medical University. From these datasets, we identified 26 key routine clinical examination indicators, including 23 blood routine parameters, 2 tumor marker indicators, and age. We constructed an IBC prediction model using 10 ML algorithms. The performance of these models was evaluated using the test set and internal validation set, with evaluation metrics including accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, F1 score, and area under the curve (AUC). Ultimately, an optimal web tool for predicting IBC was developed based on these models.
Results:
In the internal testing cohort, we assessed ten ML models. The XGBoost-based web tools emerged as the optimal choice, achieving an AUC exceeding 0.970 on both the test set and internal validation cohorts. Interpretability analysis using Shapley additive explanations (SHAP) revealed that basophils, platelet distribution width (PDW), and age features ranked highly in the feature importance of XGBoost models for IBC prediction, highlighting the importance of incorporating routinely collected clinical data into IBC prediction models.
Conclusions:
The ML-based web tool developed using 26 routine clinical examination indicators has shown considerable promise in predicting IBC. Among the models, the XGBoost algorithm exhibited the highest performance, becoming a reliable predictive tool that can enhance clinical decision-making and improve the accuracy of IBC diagnoses.