AUTHOR=Yang Huihui , Guo Yingwei , Lu Jiaxi , Hassan Haseeb , Cao Anbo , Yang Yingjian , Yassin Mazen M. , Zaman Asim , Zeng Xueqiang , Miao Xiaoqiang , Chen Ziran , Huang Guangtao , Han Taiyu , Qiu Huiling , Luo Yu , Kang Yan 

TITLE=Optimizing acute ischemic stroke outcome prediction by integrating radiomics features of DSC-PWI and perfusion parameter maps

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1528812

DOI=10.3389/fneur.2025.1528812

ISSN=1664-2295

ABSTRACT=IntroductionAccurate prediction of the prognostic outcomes for patients with ischemic stroke can contribute to personalized treatment decisions and improve life-saving outcomes. This study focuses on the performance of critical moments DSC-PWI in the prognostic prediction of acute ischemic stroke (AIS). It aims to integrate this with perfusion parameters to enhance prediction accuracy.MethodsFirstly, The radiomics technique employed to extract DSC-PWI features of critical moments and perfusion parameter features. Following this, a T-test and Lasso algorithm was used to select features associated with the prognosis. Subsequently, machine learning techniques were applied to predict the predictive outcomes of AIS patients.ResultsThe experimental results showed that DSC-PWI sequences at three critical time points—the first moment after contrast injection, the moment of minimum mean time intensity, and the last moment, collectively referred to as 3PWI, had better prognostic prediction than a single perfusion parameter, achieving an optimal model AUC of 0.863. The performance improved by 23.9, 19.6, 6, and 24% compared with CBV, CBF, MTT, and Tmax parameters. The best prognostic prediction for AIS was obtained by integrating the radiomic features from both 3PWI and perfusion parameters, resulting in the highest AUC of 0.915.DiscussionIntegrating the radiomics features of DSC-PWI sequences of three critical scan time points with those from perfusion parameters can further improve the accuracy of prognostic prediction for AIS patients. This approach may provide new insights into the prognostic evaluation of AIS and provide clinicians with valuable support in making treatment decisions.