AUTHOR=Hu Xiaolong , Deng Peng , Ma Mian , Tang Xiaoyu , Qian Jinghong , Gong YuHui , Wu Jiandong , Xu Xiaowen , Ding Zhiliang 

TITLE=A machine learning model based on results of a comprehensive radiological evaluation can predict the prognosis of basal ganglia cerebral hemorrhage treated with neuroendoscopy

JOURNAL=Frontiers in Neurology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1406271

DOI=10.3389/fneur.2024.1406271

ISSN=1664-2295

ABSTRACT=Introduction: Spontaneous intracerebral hemorrhage is the second most common subtype of stroke.Therefore, this study aimed to investigate the risk factors affecting the prognosis of patients with basal ganglia cerebral hemorrhage after neuroendoscopy.: Between January 2020 and January 2024, 130 patients with basal ganglia cerebral hemorrhage who underwent neuroendoscopy were recruited from two independent centers . We split this dataset into training (n=79), internal validation (n=22), and external validation (n=29) sets. The least absolute shrinkage and selection operator-regression algorithm was used to select the top 10 important radiomic features of different regions (perioperative hemorrhage area [PRH], perioperative surround area [PRS], postoperative hemorrhage area [PSH], and postoperative edema area [PSE]).The black hole, island, blend, and swirl signs were evaluated. The top 10 radiomic features and 4 radiological features were combined to construct the k-nearest neighbor classification (KNN), logistic regression (LR), and support vector machine (SVM) models. Finally, the performance of the perioperative hemorrhage and postoperative edema machine learning models was validated using another independent dataset (n=29).The primary outcome is mRS at 6 months after discharge. The mRS score greater than 3 defined as functional independence.