A novel clinical-radscore nomogram for predicting ruptured intracranial aneurysm

Abstract

Objectives: Our study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models.

Methods: Clinical and imaging data of 576 patients with IAs including 192 ruptured IAs and matched 384 unruptured IAs was retrospectively analyzed. Radiomics features derived from computed tomography angiography (CTA) images were selected by t-test and Elastic-Net regression. A radiomics score (radscore) was developed based on the optimal radiomics features. Inflammatory markers were selected by multivariate regression. And then 4 models including the radscore, inflammatory, clinical and clinical-radscore models (C-R model) were built. The receiver operating characteristic curve (ROC) was performed to evaluate the performance of each model, PHASES and ELAPSS. The nomogram visualizing the C-R model was constructed to predict the risk of IA rupture.

Results: Five inflammatory features, 2 radiological characteristics and 7 radiomics features were significantly associated with IA rupture. The areas under ROCs of the radscore, inflammatory, clinical and C-R models were 0.814, 0.935, 0.970 and 0.975 in the training cohort and 0.805, 0.927, 0.952 and 0.962 in the validation cohort, respectively.

Conclusion: The inflammatory model performs particularly well in predicting the risk of IA rupture, and its predictive power is further improved by combining with radiological and radiomics features and the C-R model performs the best. The C-R nomogram is a more stable and effective tool than PHASES and ELAPSS for individually predicting the risk of rupture for patients with IA.

Keywords: Computed tomography angiography; Inflammatory marker; Intracranial aneurysm; Radiomics features; Rupture.

Fig. 1

Workflow of radiomics analysis for predicting IA rupture.

Fig. 2

Feature selection with Elastic-Net regression. (A) Tuning parameter (lambda) selection in the Elastic-Net regression using 10-fold cross-validation. (B) Elastic-Net regression coefficient analysis of the 21 radiomics features. Each coloured line represents the coefficient of each feature. (C) The X-axis represents individual radiomics features, and their coefficients in the Elastic-Net regression analysis are plotted on the Y-axis.

Fig. 3

ROC curves of four models (radscore, inflammatory, clinical, and C–R model) and two scores (PHASES and ELAPSS scores). (A) The ROC curves of four models and two scores for predicting IA rupture risk in the training cohort. (B) The ROC curves of four models and two scores for predicting IA rupture risk in the validation cohort.

Fig. 4

A C–R nomogram for assessing the risk of IA rupture. The nomogram is used by first summing the points corresponding to all predictors and then find the corresponding risk of IA rupture.

Fig. 5

Calibration curves of the C–R model. (A) The calibration curve of the C–R model for predicting IA rupture risk in the training cohort. (B) The calibration curve of the C–R model for predicting IA rupture risk in the validation cohort.

Fig. 6

Decision curves of the C–R model. (A)The decision curve of the C–R model for predicting IA rupture risk in the training cohort. (B) The decision curve of the C–R model for predicting IA rupture risk in the validation cohort.