Dataset Information

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

ABSTRACT: Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

SUBMITTER: Heo D

PROVIDER: S-EPMC10449894 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Heo Donggeon D Lee Jisoo J Yoo Roh-Eul RE Choi Seung Hong SH Kim Tae Min TM Park Chul-Kee CK Park Sung-Hye SH Won Jae-Kyung JK Lee Joo Ho JH Lee Soon Tae ST Choi Kyu Sung KS Lee Ji Ye JY Hwang Inpyeong I Kang Koung Mi KM Yun Tae Jin TJ

Scientific reports 20230824 1

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning model ...[more]

PMID: 37620555

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Project description:MR Fingerprinting (MRF)-based Arterial-Spin-Labeling (ASL) has the potential to measure multiple parameters such as cerebral blood flow (CBF), bolus arrival time (BAT), and tissue T1 in a single scan. However, the previous reports have only demonstrated a proof-of-principle of the technique but have not examined the performance of the sequence in the context of key imaging parameters. Furthermore, there has not been a study to directly compare the technique to clinically used perfusion method of dynamic-susceptibility-contrast (DSC) MRI. The present report consists of two studies. In the first study (N = 8), we examined the dependence of MRF-ASL sequence on TR time pattern. Ten different TR patterns with a range of temporal characteristics were examined by both simulations and experiments. The results revealed that there was a significance dependence of the sequence performance on TR pattern (p < 0.001), although there was not a single pattern that provided dramatically improvements. Among the TR patterns tested, a sinusoidal pattern with a period of 125 TRs provided an overall best estimation in terms of spatial consistency. These experimental observations were consistent with those of numerical simulations. In the second study (N = 8), we compared MRF-ASL results with those of DSC MRI. It was found that MRF-ASL and DSC MRI provided highly comparable maps of cerebral blood flow (CBF) and bolus-arrival-time (BAT), with spatial correlation coefficients of 0.79 and 0.91, respectively. However, in terms of quantitative values, BAT obtained with MRF-ASL was considerably lower than that from DSC (p < 0.001), presumably because of the differences in tracer characteristics in terms of diffusible versus intravascular tracers. Test-retest assessment of MRF-ASL MRI revealed that the spatial correlations of parametric maps were 0.997, 0.962, 0.746 and 0.863 for B1 + , T1 , CBF, and BAT, respectively. MRF-ASL is a promising technique for assessing multiple perfusion parameters simultaneously without contrast agent.

Dataset Information

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Publications

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

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