Abstract 671P
Background
radiomics holds considerable potential in the prediction of primary lesions. A noninvasive and reliable classification of lung cancer and non-lung cancer brain metastasis types, integrated with intratumoral, peritumoral, and morphological features, could significantly aid clinicians in making earlier and more convenient individualized treatment decisions. The objective of this study was to extract MRI radiological features from our hospital's brain metastasis dataset. Furthermore, we aimed to apply various established machine learning models, leveraging augmented T1-weighted radiomics, to differentiate between brain metastases originating from lung cancer and non-lung cancer sources. Additionally, we sought to determine the optimal radiomics model for this purpose.
Methods
A retrospective analysis was conducted on 118 lung cancer brain metastases patients and 62 non-lung cancer brain metastases patients confirmed by surgery pathology or combined clinical and imaging diagnosis at the Fifth Affiliated Hospital of Sun Yat-sen University from August 2015 to September 2023. Patients were randomly divided into a training set (126 cases) and a validation set (54 cases) at a 7:3 ratio. Enhanced T1-weighted images of all patients were imported into ITK-SNAP software to manually delineate the region of interest (ROI). Radiomic features were extracted based on the ROI and feature selection was performed using the Least Absolute Shrinkage and Selection Operator. Significant features were used to develop models using logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), multilayer perceptron (MLP), and light gradient boosting machine (LightGBM). The diagnostic performance of the models was assessed using the receiver operating characteristic (ROC) curve.
Results
The LightGBM radiomics model exhibited the best diagnostic performance, with an area under the curve (AUC) of 0.875 (95% CI: 0.819–0.931) in the training set and 0.866 (95% CI: 0.740–0.993) in the validation set.
Conclusions
The enhanced MRI radiomics model, especially the LightGBM model, can accurately predict the primary lesion types of brain metastases from lung cancer and non-lung cancer origins.
Clinical trial identification
This study followed the Declaration of Helsinki and was approved by the ethics committee of the Fifth Affiliated Hospital of Sun Yat-sen University, with informed consent waived (Approval No. [2019] Ethical Letter (K03-1)).
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
The work was supported by a grant from the National Natural Science Foundation of China (Grant No: 82172881) awarded by Dr. Zibin Liang.
Disclosure
All authors have declared no conflicts of interest.