The value of nomogram based on MRI functional imaging in differentiating cerebral alveolar echinococcosis from brain metastases
Authors: Tian Pengqi , Long Changyou , Li Shuangxin , Men Miaomiao , Xing Yujie , Danzeng Yeang , Zhang Xueqian , Bao Haihua 
Published: July 2024

Abstract

Background:Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) are very similar in clinical and imaging manifestations.
Purpose:To evaluate the effectiveness of the multi-parameter nomogram model, constructed based on Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging technologies, in distinguishing CAE from BM.
Materials and Methods:Prospectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. The parameters of the parenchymal and edema areas of the lesions were compared with those of the contralateral normal brain tissue to obtain standardized values. A total of 155 lesions from the two groups were divided into a training set (108 lesions) and a test set (47 lesions), based on a 7:3 ratio, to analyze the differences between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed in the form of a nomogram. The performance of the model was comprehensively evaluated through the Receiver Operating Characteristic (ROC) curve, calibration curves (CRC), and Decision Curve Analysis (DCA).
Result:The incidence of CAE and BM differed significantly in terms of age (p < 0.001), but not gender (p = 0.539). There were no statistically significant differences in all DKI and ADL parameters between the training and test sets (all p > 0.05). Univariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. A differential diagnosis model was developed using these four independent factors and visualized through a nomogram. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. The cutoff values were 0.8266 and 0.9500, with sensitivities of 87.21% and 100.00%, and specificities of 95.45% and 95.00%, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility.
Conclusion: The nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.