Predicting Cerebral Aneurysm Rupture by Gradient Boosting Decision Tree using Clinical, Hemodynamic, and Morphological Information

摘要

Stroke is a serious cerebrovascular condition in which brain cells die due to an abrupt blockage of arteries supplying blood and oxygen or when a blood vessel bursts or ruptures and causes bleeding in the brain. Because the onset of stroke is very sudden in most people, prevention is often difficult. In Japan, stroke is one of the major causes of death and is associated with high medical costs; these problems are exacerbated by the aging population. Therefore, stroke prediction and treatment are important. The incidence of stroke may be avoided by preventive treatment based on the patient's risk of stroke. However, since judging the risk of stroke onset is largely dependent upon the individual experience and skill of the doctor, a highly accurate prediction method that is independent of the doctor's experience and skills is necessary. This study focuses on a predictive method for subarachnoid hemorrhage, which is a type of stroke. LightGBM was used to predict the rupture of cerebral aneurysms using a machine learning model that takes clinical, hemodynamic and morphological information into account. This model was used to analyze samples from 338 cerebral aneurysm cases (35 ruptured, 303 unruptured). Simulation of cerebral blood-flow was used to calculate the hemodynamic features while the surface curvature was extracted from the 3D blood-vessel-shape data as morphological features. This model yielded a sensitivity of 0.77 and a specificity of 0.83.