Dynamic Contrast-Enhanced MR Imaging of Nonenhancing T2 High-Signal-Intensity Lesions in Baseline and Posttreatment Glioblastoma: Temporal Change and Prognostic Value

Hwang, In-Kyeong; Choi, Seung Hong; Ck, Park; Kim, T M; Park, Sunghye; Won, Jae Kyung; Kim, I H; Lee, Sang Kun; Yoo, Roh‐Eul; Kang, Koung Mi; Yun, Tae Jin; Kim, Ji-Hoon; Sohn, C

doi:10.3174/ajnr.a6323

Cited by 12 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…35 In addition, the volume transfer constant in the NER could be a potential prognostic imaging biomarker in GBM. 36,37 Blood perfusion in the tumor and surrounding tissue may be related to the chemotherapeutic agent in drug delivery. 38 Yoo et al 39 reported that an enhancing lesion with a low volume transfer constant and v e (volume of extravascular extracellular space) was more likely to progress because of its low permeability or leakiness of the BBB, in which the delivery of TMZ to viable tumor cells might be less effective during standard treatment.…”

Section: Figmentioning

confidence: 99%

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

Choi

You

et al. 2021

AJNR Am J Neuroradiol

Self Cite

View full text Add to dashboard Cite

BACKGROUND AND PURPOSE: O 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in primary and recurrent glioblastoma may change during treatment. The purpose of this study was to correlate MGMT promoter methylation status changes with DWI and DSC PWI features in patients with recurrent glioblastoma after standard treatment. MATERIALS AND METHODS: Between January 2008 and November 2016, forty patients with histologically confirmed recurrent glioblastoma were enrolled. Patients were divided into 3 groups according to the MGMT promoter methylation status for the initial and recurrent tumors: 2 groups whose MGMT promoter methylation status remained, group methylated (n ¼ 13) or group unmethylated (n ¼ 18), and 1 group whose MGMT promoter methylation status changed from methylated to unmethylated (n ¼ 9). Normalized ADC and normalized relative CBV values were obtained from both the enhancing and nonenhancing regions, from which histogram parameters were calculated. The ANOVA and the Kruskal-Wallis test followed by post hoc tests were performed to compare histogram parameters among the 3 groups. The t test and Mann-Whitney U test were used to compare parameters between group methylated and group methylated to unmethylated. Receiver operating characteristic curve analysis was used to measure the predictive performance of the normalized relative CBV values between the 2 groups. RESULTS: Group methylated to unmethylated showed significantly higher means and 90th and 95th percentiles of the cumulative normalized relative CBV values of the nonenhancing region of the initial tumor than group methylated and group unmethylated (all P , .05). The mean normalized relative CBV value of the nonenhancing region of the initial tumor was the best predictor of methylation status change (P , .001), with a sensitivity of 77.78% and specificity of 92.31% at a cutoff value of 2.594. CONCLUSIONS: MGMT promoter methylation status might change in recurrent glioblastoma after standard treatment. The normalized relative CBV values of the nonenhancing region at the first preoperative MR imaging were higher in the MGMT promoter methylation change group from methylation to unmethylation in recurrent glioblastoma. ABBREVIATIONS: CCRT ¼ concurrent chemoradiation therapy; EGFR ¼ epidermal growth factor receptor; GBM ¼ glioblastoma; IDH ¼ isocitrate dehydrogenase; MGMT ¼ O 6-methylguanine-DNA methyltransferase; MM = methylated; MU ¼ methylation to unmethylation; nADC ¼ normalized ADC; NER ¼ nonenhancing region; nrCBV ¼ normalized relative CBV; rCBV ¼ relative CBV; TMZ ¼ temozolomide; UU ¼ unmethylated; VASARI ¼ Visually Accessible Rembrandt Images; WHO ¼ World Health Organization

show abstract

Section: Figmentioning

confidence: 99%

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

Choi

You

et al. 2021

AJNR Am J Neuroradiol

Self Cite

View full text Add to dashboard Cite

show abstract

“…We specified the extent of resection as maximal resection at least (i.e. including complete resection), because we focused to investigate the recurrence pattern when no measurable residual enhancing leseions were left after surgery, excluding the recurrence from measurable residual tumor 26 , 50 .…”

Section: Methodsmentioning

confidence: 99%

“…As a result, our study population was categorized into disease recurrence group ( n = 125), and non-recurrence group ( n = 67). The progression free survival (PFS) was defined as the interval between the initial diagnosis by MRI examination and the assessment of disease progression, or the last follow-up if the patient had no evidence of disease in the last follow-up 50 .…”

Section: Methodsmentioning

confidence: 99%

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Shim

Chung

Jeong

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

show abstract

“…We speci ed the extent of resection as maximal resection at least (i.e. including complete resection), because we focused to investigate the recurrence pattern when no measurable residual enhancing leseions were left after surgery, excluding the recurrence from measurable residual tumor 33,34 .…”

Section: Patientsmentioning

confidence: 99%

“…As a result, our study population was categorized into disease recurrence group (n=125), and non-recurrence group (n=67). The progression free survival (PFS) was de ned as the interval between the initial diagnosis by MRI examination and the assessment of disease progression, or the last follow-up if the patient had no evidence of disease in the last followup 33 .…”

Section: Patientsmentioning

confidence: 99%

Prediction of Recurrence Patterns in Glioblastoma Using DSC-MRI Radiomics-Based Deep Learning

Ky¹,

Sw²,

Jh³

et al. 2020

Preprint

View full text Add to dashboard Cite

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p=0.03) in multivariate Cox regression on progression-free survival.

show abstract

Dynamic Contrast-Enhanced MR Imaging of Nonenhancing T2 High-Signal-Intensity Lesions in Baseline and Posttreatment Glioblastoma: Temporal Change and Prognostic Value

Cited by 12 publications

References 32 publications

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Prediction of Recurrence Patterns in Glioblastoma Using DSC-MRI Radiomics-Based Deep Learning

Contact Info

Product

Resources

About