ABSTRACT
Stroke is a debilitating neurovascular injury that those effects hundreds of thousands of Americans each year. Despite the high prevalence, disease morbidity and mortality, options for stroke intervention and rehabilitation are still limited. Stem cells have shown promise in stroke treatment due to their ability to self-renew and differentiate into different cell types. The primary sources of stem cells used today are bone marrow and fetal brain tissue, with mesenchymal stem cells, bone marrow stem cells and neural stem cells being particularly well-studied. By secreting therapeutic and neurogenic substances they are hypothesized to help foster recovery at the site of injury. Delivery mechanisms for stem cell therapy include intracerebral, intra-arterial, intraperitoneal, intravenous, intraventricular and intranasal routes with radiographic imaging now being used to monitor the progress of stem cell therapies. Stem cell implants have been found to be safe but optimal treatment strategies are still being established with several promising studies underway. Future efforts should continue to focus on improving efficacy, exploring alternative stem cell sources, enhancing migration capability and survival and educating stroke patients on the benefits and risks of stem cell therapy.
ABSTRACT
Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized.
