Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM.
Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T1w, FLAIR and T2w sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival.
A subset of ten radiomic ‘peritumoral’ MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 × 10-5) as compared to features from enhancing tumour, necrotic regions and known clinical factors.
Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM.
• Radiomic features from peritumoral regions can capture glioblastoma heterogeneity to predict outcome.
• Peritumoral radiomics along with clinical factors are highly predictive of glioblastoma outcome.
• Identifying prognostic markers can assist in making personalized therapy decisions in glioblastoma.
KeywordsGlioblastoma multiforme Survival Radiomics Texture Peritumoral
Health Insurance Portability and Accountability Act
Karnofsky performance score
Peritumoral brain zone
The Cancer Genome Atlas
The scientific guarantor of this publication is Dr. Anant Madabhushi (Professor, Biomedical Engineering, Case Western Reserve University: email: email@example.com). The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. No complex statistical methods were necessary for this paper. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1U24CA199374-01, R21CA179327-01; R21CA195152-01, the National Institute of Diabetes and Digestive and Kidney Diseases under award number R01DK098503-02, the DOD Prostate Cancer Synergistic Idea Development Award (PC120857); the DOD Lung Cancer Idea Development New Investigator Award (LC130463), the DOD Prostate Cancer Idea Development Award; the Case Comprehensive Cancer Center Pilot Grant VelaSano Grant from the Cleveland Clinic, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University; Ohio Third Frontier Technology Validation Award; NSF-Icorps @Ohio program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The patient cohort was obtained from The Cancer Imaging Archive (TCIA). TCIA is an open archive of cancer-specific medical images and associated clinical metadata established by the collaboration between the National Cancer Institute (NCI) and participating institutions in the United States. The HIPPA compliant project in TCGA was conducted in compliance with regulations and policies for the protection of human subjects, and approvals by institutional review boards were appropriately obtained. The cohort is used for retrospective prognostic study using multi-institutional data.
- 4.Lemée J-M, Clavreul A, Menei P (2015) Intratumoral heterogeneity in glioblastoma: don’t forget the peritumoral brain zone. Neuro OncolGoogle Scholar
- 19.Laws K (1980) Textured image segmentationGoogle Scholar
- 21.Pieper S, Halle M, Kikinis R (2004) 3D SLICER. 632–635Google Scholar
- 23.Tao X, Chang M-C (2010) A skull stripping method using deformable surface and tissue classification. In: SPIEMedicalImaging. International Society for Optics and Photonics, p 76233LGoogle Scholar
- 25.Prasanna P, Dana KJ, Gucunski N et al (2014) Automated crack detection on concrete bridgesGoogle Scholar
- 27.Tiwari P, Prasanna P, Rogers L et al (2014) Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI. In: SPIEMedicalImaging. International Society for Optics and Photonics, p 90352BGoogle Scholar
- 28.De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B (2013) mRMRe: an R package for parallelized mRMR ensemble feature selection. BioinformaticsGoogle Scholar
- 33.Marko NF, Weil RJ, Schroeder JL, Lang FF, Suki D, Sawaya RE (2014) Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery. J Clin Oncol 32:774–782CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Gandrud C (2013) Reproducible research RRstudio. CRC PressGoogle Scholar
- 36.Harrell FEJ (2001) Regression modeling Strategies applications Linearmodels, Logisticregression survival analysis. Springer VerGoogle Scholar