OBJECTIVE The progression of white matter hyperintensities (WMH) varies considerably in adults. In this study, we aimed to predict the progression and related risk factors of WMH based on the radiomics of whole-brain white matter (WBWM). METHODS A retrospective analysis was conducted on 141 patients with WMH who underwent two consecutive brain magnetic resonance (MR) imaging sessions from March 2014 to May 2018. The WBWM was segmented to extract and score the radiomics features at baseline. Follow-up images were evaluated using the modified Fazekas scale, with progression indicated by scores ≥ 1. Patients were divided into progressive (n = 65) and non-progressive (n = 76) groups. The progressive group was subdivided into any WMH (AWMH), periventricular WMH (PWMH), and deep WMH (DWMH). Independent risk factors were identified using logistic regression. RESULTS The area under the curve (AUC) values for the radiomics signatures of the training sets were 0.758, 0.749, and 0.775 for AWMH, PWMH, and DWMH, respectively. The AUC values of the validation set were 0.714, 0.697, and 0.717, respectively. Age and hyperlipidemia were independent predictors of progression for AWMH. Age and body mass index (BMI) were independent predictors of progression for DWMH, while hyperlipidemia was an independent predictor of progression for PWMH. After combining clinical factors and radiomics signatures, the AUC values were 0.848, 0.863, and 0.861, respectively, for the training set, and 0.824, 0.818, and 0.833, respectively, for the validation set. CONCLUSIONS MRI-based radiomics of WBWM, along with specific risk factors, may allow physicians to predict the progression of WMH. KEY POINTS • Radiomics features detected by magnetic resonance imaging may be used to predict the progression of white matter hyperintensities. • Radiomics may be used to identify risk factors associated with the progression of white matter hyperintensities. • Radiomics may serve as non-invasive biomarkers to monitor white matter status.

中文翻译：

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来自磁共振成像的放射组学可用于预测白质高信号的进展并识别相关的危险因素。

目的 成人白质高信号 (WMH) 的进展差异很大。在本研究中，我们旨在基于全脑白质 (WBWM) 的影像组学预测 WMH 的进展和相关危险因素。方法 对 2014 年 3 月至 2018 年 5 月连续接受两次脑磁共振 (MR) 成像的 141 名 WMH 患者进行回顾性分析。 WBWM 被分割以提取基线影像组学特征并对其进行评分。使用改良的 Fazekas 量表评估随访图像，评分≥1 表示进展。患者分为进展组 (n = 65) 和非进展组 (n = 76)。进展组又细分为任何 WMH (AWMH)、脑室周围 WMH (PWMH) 和深部 WMH (DWMH)。使用逻辑回归确定独立危险因素。结果 AWMH、PWMH 和 DWMH 训练集放射组学特征的曲线下面积 (AUC) 值分别为 0.758、0.749 和 0.775。验证集的 AUC 值分别为 0.714、0.697 和 0.717。年龄和高脂血症是 AWMH 进展的独立预测因子。年龄和体重指数 (BMI) 是 DWMH 进展的独立预测因子，而高脂血症是 PWMH 进展的独立预测因子。结合临床因素和放射组学特征后，训练集的 AUC 值分别为 0.848、0.863 和 0.861，验证集的 AUC 值分别为 0.824、0.818 和 0.833。结论 基于 MRI 的 WBWM 放射组学以及特定的危险因素，可以让医生预测 WMH 的进展。要点 • 磁共振成像检测到的放射组学特征可用于预测白质高信号的进展。• 放射组学可用于识别与白质高信号进展相关的风险因素。• 放射组学可作为监测白质状态的非侵入性生物标志物。