AUTHOR=Li Linna , Li Mingyang , Chen Zhongping , Lu Fei , Zhao Min , Zhang Huimao , Tong Dan TITLE=Prognostic value of radiomics-based hyperdense middle cerebral artery sign for patients with acute ischemic stroke after thrombectomy strategy JOURNAL=Frontiers in Neurology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1037204 DOI=10.3389/fneur.2022.1037204 ISSN=1664-2295 ABSTRACT=Background and purpose

The purpose of this study was to evaluate the prognostic value of radiomics-based hyperdense middle cerebral artery sign (HMCAS) for patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT) and to establish prediction models to identify patients who may benefit more from MT.

Methods

In this retrospective study, a total of 102 consecutive patients who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent MT in our hospital between January 2019 and December 2020 were recruited. Among them, 46 experienced favorable outcomes (modified Rankin Scale [mRS] ≤ 2) at 3 months of follow-up. All patients were categorized into two sets, namely, the training set (n = 81) and the test set (n = 21). Radiomics features (RFs) and clinical features (CFs) in the training set were selected by statistical analysis to create models. The models' discriminative ability was quantified using the area under the curve (AUC) and confirmed by decision curve analyses.

Results

The prediction model established using CFs before MT includes baseline National Institutes of Health Stroke Scale (NIHSS) and neutrophil-to-lymphocyte ratio (NLR) [AUC [95% confidence interval (CI)] = 0.596 (0.312–0.881)]. A total of 1,389 RFs were extracted from each hyperdense territory and 8 RFs were left to build the radiomics model [RM; AUC (95%CI) = 0.798 (0.598–0.998)]. The model using preoperative CFs and RFs showed good performance [AUC (95%CI) = 0.817 (0.625–1.000)]. The models using post-operative CFs alone [AUC (95%CI) = 0.856 (0.685–1.000)] or post-operative CFs with RFs [AUC (95%CI) = 0.894 (0.757–1.000)] also showed good discrimination.

Conclusion

The radiomics-based HMCAS might be a promising tool to predict the prognoses of patients with AIS after MT.