Objectives To propose and evaluate a novel deep learning model for directly estimating pharmacokinetic (PK) parameter maps and uncertainty estimation from DCE-MRI. Methods In this single-center study, patients with adult-type diffuse gliomas who underwent preoperative DCE-MRI from Apr 2010 to Feb 2020 were retrospectively enrolled. A spatiotemporal probabilistic model was used to create synthetic PK maps. Structural Similarity Index Measure (SSIM) to ground truth (GT) maps were calculated. Reliability was evaluated using the intraclass correlation coefficient (ICC) for synthetic and GT PK maps. For clinical validation, Area Under the Receiver Operating Characteristic Curve (AUROC) was obtained for predicting WHO low vs high grade and IDH-wildtype vs mutant. Results 329 patients (mean age, 55 ± 15 years, 197 men) were eligible. Synthetic Ktrans, Vp, Ve maps showed high SSIM (0.961, 0.962, 0.890) compared to the GT maps. The ICC of PK maps was significantly higher in synthetic PK maps compared to the conventional approach: 1.00 vs 0.68 (p < 0.001) for Ktrans, 1.00 vs 0.59 (p < 0.001) for Vp, 1.00 vs 0.64 (p < 0.001) for Ve. PK values of enhancing tumor portion obtained from synthetic and GT maps were comparable in AUROC: (1) Ktrans, 0.857 vs 0.842 (p = 0.57); Vp, 0.864 vs 0.835 (p = 0.31); and Ve, 0.835 vs 0.830 (p = 0.88) for mutation prediction. (2) Ktrans, 0.934 vs 0.907 (p = 0.50); Vp, 0.927 vs 0.899 (p = 0.24); and Ve, 0.945 vs 0.910 (p = 0.24) for glioma grading. Conclusion Synthetic PK maps generated from DCE-MRI using a spatiotemporal probabilistic deep-learning model showed improved reliability without compromising diagnostic performance in glioma grading. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Can a deep learning model enhance the reliability of dynamic contrast-enhanced MRI (DCE-MRI) for more consistent and clinically acceptable glioma imaging? <Emphasis Type="BoldItalic">Findings</Emphasis> A spatiotemporal deep learning model outperformed the Tofts model in Ktrans reliability and preserved diagnostic performance for IDH mutation and glioma grade, bypassing arterial input function estimation. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Enhancing DCE-MRI reliability with deep learning improves imaging consistency, supports molecular tumor characterization through reproducible pharmacokinetic maps, and enables personalized treatment planning, which might lead to better clinical outcomes for patients with diffuse gliomas. Graphical Abstract

Deep learning enhances reliability of dynamic contrast-enhanced MRI in diffuse gliomas: bypassing post-processing and providing uncertainty maps

期刊论文
出版模式：

开放获取
发表日期：

2025年4月19日

查看全文

Young Wook Lyoo,
Haneol Lee,
Junhyeok Lee,
Jung Hyun Park,
Inpyeong Hwang,
Jin Wook Chung,
Seung Hong Choi,
Jaejun Yoo,
Kyu Sung Choi

摘要

Objectives

To propose and evaluate a novel deep learning model for directly estimating pharmacokinetic (PK) parameter maps and uncertainty estimation from DCE-MRI.

Methods

In this single-center study, patients with adult-type diffuse gliomas who underwent preoperative DCE-MRI from Apr 2010 to Feb 2020 were retrospectively enrolled. A spatiotemporal probabilistic model was used to create synthetic PK maps. Structural Similarity Index Measure (SSIM) to ground truth (GT) maps were calculated. Reliability was evaluated using the intraclass correlation coefficient (ICC) for synthetic and GT PK maps. For clinical validation, Area Under the Receiver Operating Characteristic Curve (AUROC) was obtained for predicting WHO low vs high grade and IDH-wildtype vs mutant.

Results

329 patients (mean age, 55 ± 15 years, 197 men) were eligible. Synthetic K^trans, Vp, Ve maps showed high SSIM (0.961, 0.962, 0.890) compared to the GT maps. The ICC of PK maps was significantly higher in synthetic PK maps compared to the conventional approach: 1.00 vs 0.68 (p < 0.001) for K^trans, 1.00 vs 0.59 (p < 0.001) for Vp, 1.00 vs 0.64 (p < 0.001) for Ve. PK values of enhancing tumor portion obtained from synthetic and GT maps were comparable in AUROC: (1) K^trans, 0.857 vs 0.842 (p = 0.57); Vp, 0.864 vs 0.835 (p = 0.31); and Ve, 0.835 vs 0.830 (p = 0.88) for mutation prediction. (2) K^trans, 0.934 vs 0.907 (p = 0.50); Vp, 0.927 vs 0.899 (p = 0.24); and Ve, 0.945 vs 0.910 (p = 0.24) for glioma grading.

Conclusion

Synthetic PK maps generated from DCE-MRI using a spatiotemporal probabilistic deep-learning model showed improved reliability without compromising diagnostic performance in glioma grading.

Key Points

Question Can a deep learning model enhance the reliability of dynamic contrast-enhanced MRI (DCE-MRI) for more consistent and clinically acceptable glioma imaging?

Findings A spatiotemporal deep learning model outperformed the Tofts model in Ktrans reliability and preserved diagnostic performance for IDH mutation and glioma grade, bypassing arterial input function estimation.

Clinical relevance Enhancing DCE-MRI reliability with deep learning improves imaging consistency, supports molecular tumor characterization through reproducible pharmacokinetic maps, and enables personalized treatment planning, which might lead to better clinical outcomes for patients with diffuse gliomas.

Graphical Abstract