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Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

ABSTRACT: BACKGROUND AND PURPOSE:Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. MATERIALS AND METHODS:A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. RESULTS:Age (P < .001, hazard ratio = 2.11, c-index = 0.705), surgery (P < .001, hazard ratio = 2.04, c-index = 0.712), contrast-enhancing rim width (P < .001, hazard ratio = 2.15, c-index = 0.704), and surface regularity (P = .021, hazard ratio = 1.66, c-index = 0.639) measured on the largest focus were significant independent predictors of survival. Maximum contrast-enhancing rim width (P = .002, hazard ratio = 2.05, c-index = 0.668) and minimal surface regularity (P = .036, hazard ratio = 1.64, c-index = 0.600) were also significant. A multivariate model using age, surgery, and contrast-enhancing rim width measured on the largest foci classified multifocal glioblastomas into groups with different outcomes (P < .001, hazard ratio = 3.00, c-index = 0.853, median survival difference = 10.55 months). Moreover, quartiles with the highest and lowest individual prognostic scores based on the focus with the largest volume and surgery were identified as extreme groups in terms of survival (P < .001, hazard ratio = 18.67, c-index = 0.967). CONCLUSIONS:A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.

SUBMITTER: Perez-Beteta J

PROVIDER: S-EPMC7048517 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

Pérez-Beteta J J Molina-García D D Villena M M Rodríguez M J MJ Velásquez C C Martino J J Meléndez-Asensio B B Rodríguez de Lope Á Á Morcillo R R Sepúlveda J M JM Hernández-Laín A A Ramos A A Barcia J A JA Lara P C PC Albillo D D Revert A A Arana E E Pérez-García V M VM

AJNR. American journal of neuroradiology 20190328 4

<h4>Background and purpose</h4>Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously.<h4>Materials and methods</h4>A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclus ...[more]

PMID: 30923085

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Project description:BackgroundCardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features.PurposeTo investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images.Study typeProspective.PopulationA total of 11 healthy participants/five patients.Field strength/ sequenceA 3 T/cine balanced steady-state free-precession, T1 -weighted spoiled gradient-echo, T2 -weighted turbo spin-echo, and quantitative T1 and T2 mapping. For each sequence, the flip angle, in-plane resolution, slice thickness, and parallel imaging technique were varied to study the sensitivity of radiomic features to alterations in imaging parameters.AssessmentMyocardial contours were manually delineated by experienced readers, and a total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families.Statistical testsSensitivity was defined as the standardized mean difference (D effect size), and the robust features were defined at sensitivity < 0.2. Sensitivity analysis was performed on predefined sets of reproducible features. The analysis was performed using the entire cohort of 16 subejcts.Results64% of radiomic features were robust (sensitivity < 0.2) to changes in any imaging parameter. In qualitative sequences, radiomic features were most sensitive to changes in in-plane spatial resolution (spatial resolution: 0.6 vs. flip angle: 0.19, parallel imaging: 0.18, slice thickness: 0.07; P < 0.01 for all); in quantitative sequences, radiomic features were least sensitive to changes in spatial resolution (spatial resolution: 0.07 vs. slice thickness: 0.16, flip angle: 0.24; P < 0.01 for all). In an individual feature level, no singular feature family/image filter was identified as robust (sensitivity < 0.2) across sequences; however, highly sensitive features were predominantly associated with high-frequency wavelet filters across all sequences (32/50 features).Data conclusionIn cardiac MR, a considerable number of radiomic features are sensitive to changes in sequence parameters.Evidence level1 TECHNICAL EFFICACY: Stage 1.

Dataset Information

Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

Publications

Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

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