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

Abstract

Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. Purpose This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. Methods We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. 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, k-nearest neighbors classifier, and Gaussian naïve bias 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 support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (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 based 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.

Comparison of clinicopathological features between cerebral cystic and alveolar echinococcosis: analysis of 27 cerebral echinococcosis cases in Xinjiang, China

“…In MRI scans, the CCE lesions have clear boundaries with surrounding tissues, and there is usually no edema zone around foci [ 13 ]. While CAE lesions have some imaging features that differ from CCE [ 6 , 14 ]: the lesions show equal or slightly higher signal intensity on T1WI, and low signal intensity on T2WI, with multiple small cysts within the lesions, which are surrounded by broad edema bands. Besides, there is no significant enhancement of the lesions after MRI enhancement scanning in CCE cases, while CAE lesions show nodular, honeycomb, or cauliflower-like irregular enhancement at the margins of the lesion [ 15 ], which significantly distinguishes CAE from CCE.…”