title: Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.
abstract: BACKGROUND
  Glioblastoma 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.
  
  
  OBJECTIVE
  To 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.
  
  
  METHODS
  Preoperative 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.
  
  
  RESULTS
  This 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.
  
  
  CONCLUSIONS
  Visually 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.
authors.affiliation: 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.
authors.forename: Hamed
authors.initials: H
authors.lastname: Akbari
authors.forename: Luke
authors.initials: L
authors.lastname: Macyszyn
authors.forename: Xiao
authors.initials: X
authors.lastname: Da
authors.forename: Michel
authors.initials: M
authors.lastname: Bilello
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Wolf
authors.forename: Maria
authors.initials: M
authors.lastname: Martinez-Lage
authors.forename: George
authors.initials: G
authors.lastname: Biros
authors.forename: Michelle
authors.initials: M
authors.lastname: Alonso-Basanta
authors.forename: Donald M
authors.initials: DM
authors.lastname: OʼRourke
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001932:Brain Neoplasms / Q000473:pathology* / Q000601:surgery; D015331:Cohort Studies; D056324:Diffusion Tensor Imaging; D005260:Female; D005909:Glioblastoma / Q000473:pathology* / Q000601:surgery; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000473:pathology / Q000601:surgery; D059906:Neuroimaging; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies
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