Research Paper Volume 13, Issue 9 pp 13195—13210

Morphology-based radiomics signature: a novel determinant to identify multiple intracranial aneurysms rupture

The area under the curves (AUCs) shows that the morphology-based radiomics signature model (A) has better discrimination compared with the morphology-based radiomics features model (B) and morphology-based radiomics features model (C). Radiomics morphological feature selection used the LASSO binary logistic regression model (D).

Figure 7. The area under the curves (AUCs) shows that the morphology-based radiomics signature model (A) has better discrimination compared with the morphology-based radiomics features model (B) and morphology-based radiomics features model (C). Radiomics morphological feature selection used the LASSO binary logistic regression model (D).