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J Med Imaging (Bellingham). 2018 Apr;5(2):021219. doi: 10.1117/1.JMI.5.2.021219. Epub 2018 Mar 1.

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

Author information

1
University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
2
University of Pennsylvania, Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
3
Thomas Jefferson University, Department of Radiation Oncology, Philadelphia, Pennsylvania, United States.
4
University of Pennsylvania, Department of Radiation Oncology, Perelman School of Medicine, Philadelphia, Pennsylvania, United States.

Abstract

Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

KEYWORDS:

glioblastoma; machine learning; radiomics; tumor infiltration; tumor recurrence

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