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From the Mathematical Oncology Laboratory, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo Jose Cela 3, 13071 Ciudad Real, Spain (J.P., D.M., J.A.O., A.F., B.L., V.M.P.); Departments of Radiology and Neurosurgery, Hospital General de Ciudad Real, Ciudad Real, Spain (E. Arregui, M.C., J.M.B.); Departments of Pathology, Radiology, and Neurosurgery, Hospital Virgen de la Salud, Toledo, Spain (B.M., Á.R.d.L., R.M.d.l.P.); Department of Neurosurgery, Hospital Clínico San Carlos, Madrid, Spain (L.I.B., J.A.B.); Department of Neurosurgery, Hospital Marqués de Valdecilla, Santander, Spain (J.M., C.V.); Department of Radiology, Complejo Hospitalario Universitario de Granada, Granada, Spain (B.A.); Department of Radiology, Hospital Carlos Haya, Málaga, Spain (M.B., I.H.); Department of Radiology, Hospital de Manises, Valencia, Spain (A.R.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (E. Arana).
Published: July 2018
AI Article Synopsis
Article Abstract
Purpose To evaluate the prognostic and predictive value of surface-derived imaging biomarkers obtained from contrast material-enhanced volumetric T1-weighted pretreatment magnetic resonance (MR) imaging sequences in patients with glioblastoma multiforme. Materials and Methods A discovery cohort from five local institutions (165 patients; mean age, 62 years ± 12 [standard deviation]; 43% women and 57% men) and an independent validation cohort (51 patients; mean age, 60 years ± 12; 39% women and 61% men) from The Cancer Imaging Archive with volumetric T1-weighted pretreatment contrast-enhanced MR imaging sequences were included in the study. Clinical variables such as age, treatment, and survival were collected. After tumor segmentation and image processing, tumor surface regularity, measuring how much the tumor surface deviates from a sphere of the same volume, was obtained. Kaplan-Meier, Cox proportional hazards, correlations, and concordance indexes were used to compare variables and patient subgroups. Results Surface regularity was a powerful predictor of survival in the discovery (P = .005, hazard ratio [HR] = 1.61) and validation groups (P = .05, HR = 1.84). Multivariate analysis selected age and surface regularity as significant variables in a combined prognostic model (P < .001, HR = 3.05). The model achieved concordance indexes of 0.76 and 0.74 for the discovery and validation cohorts, respectively. Tumor surface regularity was a predictor of survival for patients who underwent complete resection (P = .01, HR = 1.90). Tumors with irregular surfaces did not benefit from total over subtotal resections (P = .57, HR = 1.17), but those with regular surfaces did (P = .004, HR = 2.07). Conclusion The surface regularity obtained from high-resolution contrast-enhanced pretreatment volumetric T1-weighted MR images is a predictor of survival in patients with glioblastoma. It may help in classifying patients for surgery.
