Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Abstract

Objective: Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods: From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results: A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion: Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

Keywords: middle cerebral artery; morphology; random forest; rupture; small aneurysm.

Figure 1

The flowchart of this study.

Figure 2

Measurements of aneurysm morphological parameters.

Figure 3

(A–C) Receiver operating characteristic (ROC) curves of the random forest and logistic regression models in training, internal, and external validation cohort. (D) The performance of the random forest and logistic regression models to predict the rupture of small middle cerebral artery (MCA) aneurysms. AUC, area under the receiver operating curve; CI, confidence interval; ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; and NPV, negative predictive value.

Figure 4

A nomogram for predicting small middle cerebral artery aneurysm rupture. The nomogram incorporated five attributes: SR, multi aneurysms, daughter dome, AR, and aneurysm angle. To use the nomogram, read the scoring points from the “Point” reference line in line with the variable, add the points from all the variables, and find the predicted probability of rupture risk at the bottom “Risk” line. AR, aspect ratio; SR, size ratio.