Novel and Efficient Quantitative Posterior-Circulation-Structure-Based Scale via Noncontrast CT to Predict Ischemic Stroke Prognosis: A Retrospective Study

Abstract

(1) Background: Posterior circulation ischemic stroke has high mortality and disability rates and requires an early prediction prognosis to provide the basis for an interventional approach. Current quantitative measures are only able to accurately assess the prognosis of patients using magnetic resonance imaging (MRI). However, it is difficult to obtain MRI images in critically urgent cases. Therefore, the development of a noncontrast CT-based rapid-assist tool is needed to enhance the value of the clinical application. (2) Objective: This study aimed to develop an auxiliary-annotating noncontrast CT-efficient tool, which is based on a deep learning model, to provide a quantitative scale and the prognosis of posterior circulation ischemic stroke patients. (3) Methods: A total of 31 patients with posterior circulation ischemic stroke, diagnosed in the stroke registry at the Tri-Service General Hospital from November 2019 to July 2020, were included in the study, with a total of 578 CT images collected from noncontrast CT and MRI that were ≤ 3 days apart. A 5-fold cross validation was used to develop an image segmentation model to identify nine posterior circulation structures, and intersection over union (IoU) was used to assess the ability of the model to identify each structure. A quantitative score was integrated to assess the importance of the proportion of ischemic lesions in each posterior circulation structure, and the ROC curve was compared with the semiquantitative score for prognostic power. The prognoses of the patients were defined into two groups of 18 patients. An mRS score of 0–2 at discharge was defined as a good prognosis, while an mRS score of 3–6 was deemed to be a poor prognosis. (4) Results: The performance of the image segmentation model for identifying the nine posterior circulation structures in noncontrast CT images was evaluated. The IoU of the left cerebellum was 0.78, the IoU of the right cerebellum was 0.79, the IoU of the left occipital lobe was 0.74, the IoU of the right occipital lobe was 0.68, the IoU of the left thalamus was 0.73, the IoU of the right thalamus was 0.75, the IoU of the medulla oblongata was 0.82, and the IoU of the midbrain was 0.83. The prognostic AUC of posterior circulation patients predicted using a quantitative integrated score was 0.74, which was significantly higher than that of the pc-ASPECTS (AUC = 0.63, p = 0.035), with a sensitivity of 0.67 and a specificity of 0.72. (5) Conclusions: In this study, a deep learning model was used to develop a noncontrast CT-based quantitative integrated score tool, which is an effective tool for clinicians to assess the prognosis of posterior circulation ischemic stroke.

Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review

“…In 17 of the studies, the primary focus was on predicting the mRS based on data available during the acute phase of stroke. However, two other studies (Fang et al, 2022;Meng et al, 2022) did not explicitly mention the phase from which their data was obtained.…”

“…The 90 day mRS is the most frequent choice, being used in 16 works. The exceptions were Mah et al (2020) and Fang et al (2022) who considered the discharge mRS and Danala et al (2022) who considered the mRS after EVT.…”

“…The DL algorithms all got an AUC bellow 0.8. Hilbert et al (2019) and Fang et al (2022) both used 2D neural networks. The former transformed the 3D CT volume into a 2D projection using maximum intensity projection (MIP) (Fishman et al, 2006) and the latter worked at the axial slice level.…”

“…A segmentation model for nine posterior circulation structures was developed by Fang et al (2022). The ground truth masks for this model were annotated by a neurologist.…”