A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer.

backgroundTriple-negative breast cancer (TNBC) patients have a high 2-year post-operative incidence of brain metastasis (BM). Currently, there is no early prediction tool to predict the risk of BM in TNBC patients.

methodsData of breast cancer patients, who had been scanned, resected, and pathologically diagnosed at a local hospital from May 2012 to June 2018 were collected. Primary and radiological secondary exclusion criteria were used to determine patients' eligibility for inclusion in the study. Data for the TNBC cohort included qualified 2-year post-operative follow-up information, BM status, and pre-operative MRI data. Age-based propensity score matching (PSM) was used to build a comparable study cohort. The tumor regions of interest were segmented and used for lattice radiomics feature extraction. The filtered and normalized lattice radiomics features were then trained with BM status using the random forest (RF), support vector machine (SVM), k-nearest neighbor, least absolute shrinkage and selection operator regression, naïve Bayesian, and neural network algorithms. The generated prediction models were evaluated using 10-fold cross verification, and the areas under the curve (AUCs), accuracy, sensitivity, and specificity were reported.

resultsData from 643 breast cancer patients were collected. Among these, 84 TNBC cases (comprising 42 pairs) were included in this study after primary exclusion, radiological secondary exclusion, and PSM. We extracted 3,854 lattice radiomics features from the pre-operative MRI. Of these, 2,480 were used for model training after filtration. The 10-fold verification results showed that the BM risk-prediction model, which was based on the normalized and filtered lattice radiomics features of collected cases trained by naïve Bayesian algorithm, had a high AUC (0.878), accuracy (0.786), specificity (81.0%), and sensitivity (76.2%).

conclusionsThe pre-operative MRI data of TNBC patients can be used to predict 2-year BM risk. This application could help to achieve better early stratification, BM screening, and the overall prognosis.