将弥散加权和灌注加权 MRI 纳入放射组学模型可提高胶质母细胞瘤患者假性进展的诊断性能。
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.
机构信息
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Deparment of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
出版信息
Neuro Oncol. 2019 Feb 19;21(3):404-414. doi: 10.1093/neuonc/noy133.
BACKGROUND
Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.
METHODS
The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).
RESULTS
Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.
CONCLUSIONS
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.
背景
假性进展是治疗后早期胶质母细胞瘤的诊断难题。因此，我们开发并验证了一种使用多参数 MRI 的放射组学模型，以区分胶质母细胞瘤患者治疗后 3 个月内的假性进展和早期肿瘤进展。
方法
该模型由 61 例接受标准治疗后 3 个月内扩大的对比增强部分构建，从对比增强 T1 加权成像、液体衰减反转恢复成像以及表观扩散系数（ADC）和脑血容量（CBV）图中获得 6472 个放射组学特征。使用 10 折交叉验证的 LASSO（最小绝对收缩和选择算子）逻辑回归模型选择成像特征。使用受试者工作特征曲线下面积（AUC）比较假性进展的诊断性能与单一参数（平均和最小 ADC 以及平均和最大 CBV）和单一成像放射组学模型。该模型通过外部队列（n=34）和内部前瞻性注册数据（n=23）进行验证。
结果
12 个有意义的放射组学特征（3 个来自常规 MRI，2 个来自扩散 MRI，7 个来自灌注 MRI）被用于模型构建。多参数放射组学模型（AUC，0.90）的性能明显优于任何单一 ADC 或 CBV 参数（AUC，0.57-0.79，P<0.05），也优于使用常规 MRI（AUC，0.76，P=0.012）、ADC（AUC，0.78，P=0.014）或 CBV（AUC，0.80，P=0.43）的单一放射组学模型。多参数放射组学模型在外部验证（AUC，0.85）和内部验证（AUC，0.96）中的表现均优于任何单一方法，因此具有稳健性。
结论
将扩散加权和灌注加权 MRI 纳入放射组学模型可提高识别假性进展的诊断性能，并在多中心环境中具有稳健性。
Zotero 插件