• Hamed Akbari, Luke Macyszyn, Xiao Da, Michel Bilello, Ronald L Wolf, Maria Martinez-Lage, George Biros, Michelle Alonso-Basanta, Donald M OʼRourke, and Christos Davatzikos.
• Departments of ‡Radiology, §Neurosurgery, ¶Pathology and Laboratory Medicine, and ‖Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; #Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas; **Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
• Neurosurgery. 2016 Apr 1; 78 (4): 572-80.

BackgroundGlioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor.ObjectiveTo develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival.MethodsPreoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. 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.ResultsThis technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning.ConclusionVisually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

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