# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 1 Introduction  
The increasing credibility of computational models and simulations of medical treatments is encouraging the exploration of the use of in silico trials to support and refine traditional clinical trials. An in silico trial consists of using computational tools to simulate the use of a new treatment, device, or drug in a cohort of virtual patients, obtaining insights on the efficacy. Credible biomechanical models have the potential of reducing the overall time and costs of the process by estimating in advance the performance of the new devices and only letting the most promising ones move forward to actual clinical trials [1]. Many clinical applications would benefit from the development of in silico trials. Among these, an in silico trial may support the development of better treatments for acute ischemic stroke (AIS), the pathology occurring when a thrombus obstructs a cerebral artery causing the development of an infarct zone in brain tissues [2]. In the case of large vesselocclusion, i.e., occurring in the internal carotid artery (ICA), the middle cerebral artery (MCA) M1 or M2 segments, or the anterior cerebral artery (ACA), which form a bifurcation called the T-junction, the most effective treatment is endovascular thrombectomy [3], a minimally invasive treatment aiming at mechanically removing the thrombus with a stent-retriever and/or aspiration catheters. High-fidelity in silico models of the thrombectomy procedure have been recently proposed in the literature [4; 5; 6; 7]. The stent-retriever thrombectomy simulation developed in [4], used to replicate a patient-specific case, was integrated into a framework for running the first proof-of-concept in silico trial for stroke treatments [8]. The framework was validated by replicating a real clinical trial and then employed to compare treatment outcomes in different subpopulations and with different thrombectomy devices, demonstrating the potentiality of the in silico approach to support clinical stroke trials. Knowing that anatomy does impact thrombectomy outcome [9; 10; 11; 12; 13], two aspects are important to consider when developing in silico trials for stroke treatments: (i) patient stratification based on anatomical characteristics; (ii) the generation of a large cohort of patients. The latter aspect is important for building data-driven surrogate or reduced-order models to predict in silico trial outcomes. Normally, in patient-specific modeling, the geometry domain is obtained from diagnostic images, e.g., computed tomography (CT) and Magnetic Resonance Imaging (MRI). In the AIS context, it is possible to automatically identify the centerlines and diameters of the vessels [14], while three-dimensional (3D) reconstruction from the segmentation process provides a non-smooth voxel representation and requires additional steps to be used in models [4]. Due to the limited accessibility, consistency, and quality of clinical images, the generation of virtual cohorts of patients is helpful to run in silico clinical trials. When an in silico trial is based solely on data-driven models, the cohort of patients can be represented by a set of metadata [8]. Differently, if the in silico trial pipeline includes high-fidelity modeling (e.g., [15; 16]), the cohort of patients requires the collection of 3D domains. In [17], the authors provided a review of different virtual cohorts of cardiac models. They indicated three different methodologies for developing virtual patient cohorts: mapping virtual cohorts, sampling from inferred distributions, and random sampling with acceptance criteria. In the current literature, an acceptance criterion for a virtual patient cohort can be distinguished as data-driven or clinically driven: data-driven if the acceptance criterion relies on the geometrical variability of the training samples [18]; clinically driven if the acceptance criterion is defined by a clinical requirement, for example, a specific characteristic or phenotype [19].