Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model

Abstract

Background: Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors.

Methods: A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis.

Results: A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model.

Conclusion: Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

Fig. 1

Flowchart of patient inclusion and exclusion process.

Fig. 2

Study flowchart.

Fig. 3

Radiomics signature score (rad-score) calculation. Top ten features and feature coefficients (feature importance) between the ruptured status of aneurysms (A–B). The radiomics scores (rad-scores) of each patient in the training (C) and test cohorts (D) showed the association of a high rad-score with the risk of aneurysm rupture.

Fig. 4

Receiver operating characteristic (ROC) curves and calibration curve of the radiomics model (R model), clinical model (C model), and clinical radiomics model (CR model). The operating characteristic curves of the three models in the training (A) and test cohorts (B). Calibration curve of the three models in the training (C) and test cohorts (D).

Fig. 5

Decision curve analysis and comprehensive nomogram for small aneurysms in all patients. (A) Training and test cohorts (B) decision curve analysis of the clinical model, radiomics model, and clinical–radiomics model with the threshold probability on the x-axis and the net benefit on the y-axis. Nomogram for the prediction of small aneurysm ruptures (C).