Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China.
Inner Mongolia Medical University, Hohhot, 010110, China.
Eur Radiol. 2023 Dec;33(12):8809-8820. doi: 10.1007/s00330-023-09861-0. Epub 2023 Jul 13.
To develop and validate a radiomics-based model (ADGGIP) for predicting adult-type diffuse gliomas (ADG) grade by combining multiple diffusion modalities and clinical and imaging morphologic features.
In this prospective study, we recruited 103 participants diagnosed with ADG and collected their preoperative conventional MRI and multiple diffusion imaging (diffusion tensor imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and mean apparent propagator diffusion-MRI) data in our hospital, as well as clinical information. Radiomic features of the diffusion images and clinical information and morphological data from the radiological reports were extracted, and multiple pipelines were used to construct the optimal model. Model validation was performed through a time-independent validation cohort. ROC curves were used to evaluate model performance. The clinical benefit was determined by decision curve analysis.
From June 2018 to May 2021, 72 participants were recruited for the training cohort. Between June 2021 and February 2022, 31 participants were enrolled in the prospective validation cohort. In the training cohort (AUC 0.958), internal validation cohort (0.942), and prospective validation cohort (0.880), ADGGIP had good accuracy in predicting ADG grade. ADGGIP was also significantly better than the single-modality prediction model (AUC 0.860) and clinical imaging morphology model (0.841) (all p < .01) in the prospective validation cohort. When the threshold probability was greater than 5%, ADGGIP provided the greatest net benefit.
ADGGIP, which is based on advanced diffusion modalities, can predict the grade of ADG with high accuracy and robustness and can help improve clinical decision-making.
Integrated multi-modal predictive modeling is beneficial for early detection and treatment planning of adult-type diffuse gliomas, as well as for investigating the genuine clinical significance of biomarkers.
• Integrated model exhibits the highest performance and stability. • When the threshold is greater than 5%, the integrated model has the greatest net benefit. • The advanced diffusion models do not demonstrate better performance than the simple technology.
通过结合多种扩散模态和临床及影像学形态学特征，开发并验证一种基于放射组学的模型（ADGGIP），以预测成人弥漫性神经胶质瘤（ADG）的分级。
本前瞻性研究纳入了在我院诊断为 ADG 的 103 名参与者，收集了他们术前的常规 MRI 及多种扩散成像（弥散张量成像、扩散峰度成像、神经纤维方向分散和密度成像、平均表观扩散系数成像）数据，以及临床信息。提取扩散图像和临床信息以及放射学报告形态学数据的放射组学特征，并采用多种流水线构建最优模型。通过独立的时间验证队列进行模型验证。采用 ROC 曲线评估模型性能。通过决策曲线分析确定临床获益。
2018 年 6 月至 2021 年 5 月，纳入 72 名参与者作为训练队列。2021 年 6 月至 2022 年 2 月，纳入 31 名参与者作为前瞻性验证队列。在训练队列（AUC 0.958）、内部验证队列（0.942）和前瞻性验证队列（0.880）中，ADGGIP 对 ADG 分级的预测具有良好的准确性。ADGGIP 在前瞻性验证队列中也明显优于单一模式预测模型（AUC 0.860）和临床影像形态学模型（0.841）（均 P<.01）。当阈值概率大于 5%时，ADGGIP 提供了最大的净获益。
ADGGIP 基于先进的扩散模态，可准确、稳健地预测 ADG 分级，有助于改善临床决策。
综合多模态预测模型有利于成人弥漫性神经胶质瘤的早期检测和治疗计划，以及研究生物标志物的真实临床意义。
· 综合模型具有最高的性能和稳定性。
· 当阈值大于 5%时，综合模型具有最大的净收益。
· 先进的扩散模型并未表现出优于简单技术的性能。
