Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany.
Eur J Neurol. 2021 Sep;28(9):2989-3000. doi: 10.1111/ene.15000. Epub 2021 Jul 18.
Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT).
We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.
In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts.
Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.
放射组学为从医学图像中自动提取高维特征集提供了一个框架。我们旨在确定基线头部 CT 上颅内出血（ICH）病灶的放射组学特征与入院临床严重程度和中期结局的相关性。
我们使用了 ATACH-2（急性脑出血降压治疗 II）试验数据集。本分析纳入的患者（n=895）被随机分配到发现队列（n=448）和独立验证队列（n=447）。我们从基线非对比头部 CT 扫描的血肿病灶中提取了 1130 个放射组学特征，并生成了与入院格拉斯哥昏迷量表（GCS）、入院国立卫生研究院卒中量表（NIHSS）和 3 个月改良 Rankin 量表（mRS）评分相关的放射组学特征。将放射组学特征与相应的目标变量之间的 Spearman 相关性与血肿体积进行了比较。
在发现队列中，与 ICH 体积相比，放射组学特征与入院 GCS（0.47 对 0.44，p=0.008）、入院 NIHSS（0.69 对 0.57，p<0.001）和 3 个月 mRS 评分（0.44 对 0.32，p<0.001）的相关性更强。同样，在独立验证中，与 ICH 体积相比，放射组学特征与入院 GCS（0.43 对 0.41，p=0.02）、NIHSS（0.64 对 0.56，p<0.001）和 3 个月 mRS 评分（0.43 对 0.33，p<0.001）的相关性更强。在调整了 ICH 结局的已知预测因子的多元回归分析中，放射组学特征在两个队列中都是 3 个月 mRS 的独立预测因子。
受 ATACH-2 试验纳入标准的限制，我们表明，在基线 CT 上量化血肿纹理、密度和形状的放射组学特征可为临床表现和 3 个月结局提供影像学相关性。这些发现可能会引发一种范式转变，即影像学生物标志物可能会改进目前用于 ICH 患者预后、风险分层和治疗分诊的模型。
