Dataset Information

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

ABSTRACT:

Purpose

To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis.

Materials and methods

This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort.

Results

The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409).

Conclusion

The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.

SUBMITTER: Cui Y

PROVIDER: S-EPMC4734164 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

Cui Yi Y Tha Khin Khin KK Terasaka Shunsuke S Yamaguchi Shigeru S Wang Jeff J Kudo Kohsuke K Xing Lei L Shirato Hiroki H Li Ruijiang R

Radiology 20150904 2

<h4>Purpose</h4>To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis.<h4>Materials and methods</h4>This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the C ...[more]

PMID: 26348233

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Project description:OBJECTIVES:The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients. METHODS:Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals-a texture analysis method capturing heterogeneity of MR images-were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves. RESULTS:Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model-having clinical and Minkowski features-was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22). CONCLUSIONS:Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.

Project description:BackgroundTo assess the quantification of tumor burden in multiple myeloma (MM) patients using whole-body magnetic resonance imaging (MRI) and to identify the correlation between MRI parameters and prognostic biomarkers.MethodsWe retrospectively analyzed 95 newly diagnosed MM patients treated at our hospital from June 2018 to March 2020. All patients underwent whole-body MRI examination, including diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging (mDIXON), and short TI inversion recovery (STIR) sequences. The MRI presentation was used to determine MM infiltration patterns and calculate apparent diffusion coefficient (ADC) and a fat fraction (FF). The one-way ANOVA and Kruskal-Wallis test were used to compare the differences of these values between DS, ISS, and R-ISS stages in different MM infiltration patterns. Spearman correlation test was used for correlation analysis of ADC and FF against prognostic biomarkers, and two independent sample t-test was used to evaluate the differences of ADC and FF in different free light-chain ratio groups.ResultsThe MRI presentation was classified into normal pattern (36 patients; 37.9%), diffuse (27 patients; 28.4%), and focal (32 patients; 33.7%) infiltration patterns. Statistically significant ADC and FF differences between different DS, ISS, and R-ISS stages were observed in normal/diffuse infiltration patterns but not in focal infiltration patterns. The ADC and FF of the normal/diffuse infiltration pattern showed correlations with hemoglobin, β2-microglobulin, bone marrow plasma cells, flow cytometry of bone marrow cells, and serum monoclonal protein. In contrast, ADC in focal infiltration patterns was negatively correlated with β2-microglobulin and C-reactive protein. The FF of patients with a normal/diffuse infiltration pattern was higher in the low free light-chain ratio group than that in the high free light-chain ratio group (P=0.023).ConclusionsOur observations indicate that quantitative whole-body functional MRI examination may serve as an effective complement to imaging diagnosis based on morphology and provide further information on the tumor burden of patients with MM.

Dataset Information

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

Purpose

Materials and methods

Results

Conclusion

Publications

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

Similar Datasets

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