Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

Abstract

Abstract Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) are similar in locations and imaging appearance. While, CAE is usually treated with chemotherapy and surgical treatment, BM is often treated with radiotherapy and targeted primary malignancy treatment. Accurate diagnosis is critical due to the vastly different treatment approaches for these conditions. Purpose This study aims to investigate the effectiveness of radiomics and machine learning approaches on magnetic resonance imaging (MRI) in distinguishing CAE and BM. Methods We have retrospectively analyzed MRI images of 130 patients (30 CAE, 100 BM, training set = 91, testing set = 39) who confirmed CAE or BM in Xinjiang medical university's first affiliated hospital from January 2014 to December 2022. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, KNeighbors classifier, and Gaussian NB and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). Results The area under curve (AUC) of SVC, LR, LDA, KNN, and NB algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93) respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. Conclusion The combination of radiomics and machine learning approach on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making