The objective of this study is to develop a machine-learning model that can accurately distinguish between different histologic types of brain lesions in patients with non-small cell lung cancer (NSCLC) when it is not safe or feasible to perform a biopsy. To achieve this goal, the study utilized data from two patient cohorts: 116 patients from Xiangya Hospital and 35 patients from Yueyang Central Hospital. A total of eight machine learning algorithms, including Xgboost, were compared. Additionally, a 3-dimensional convolutional neural network was trained using transfer learning to further evaluate the performance of these models. The SHapley Additive exPlanations (SHAP) method was developed to determine the most important features in the best-performing model after hyperparameter optimization. The results showed that the area under the curve (AUC) for the classification of brain lesions as either lung adenocarcinoma or squamous carcinoma ranged from 0.60 to 0.87. The model based on single radiomics features extracted from contrast-enhanced T1 MRI and utilizing the Xgboost algorithm demonstrated the highest performance (AUC: 0.85) in the internal validation set and adequate performance (AUC: 0.80) in the independent external validation set. The SHAP values also revealed the impact of individual features on the classification results. In conclusion, the use of a radiomics model incorporating contrast-enhanced T1 MRI, Xgboost, and SHAP algorithms shows promise in accurately and interpretably identifying brain lesions in patients with NSCLC.
Keywords: Brain metastasis; Explain ability; Machine learning; Magnetic resonance imaging; Radiomics; Radiotherapy.
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