title: A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation.
abstract: We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.
  
  
  
  Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2-3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.
  
  
  
  While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (rnecrotic= 0.92, rperi-necrotic= 0.82 and rviable= 0.98) with the histology.
  
  
  
  The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.
authors.affiliation: Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany. prateek.katiyar@med.uni-tuebingen.de.|Max Planck Institute for Intelligent Systems, Tuebingen, Germany. prateek.katiyar@med.uni-tuebingen.de.
authors.forename: Prateek
authors.identifier: 0000-0002-3062-9366
authors.initials: P
authors.lastname: Katiyar
authors.affiliation: Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
authors.forename: Mathew R
authors.initials: MR
authors.lastname: Divine
authors.affiliation: Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany.
authors.forename: Ursula
authors.initials: U
authors.lastname: Kohlhofer
authors.affiliation: Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany.
authors.forename: Leticia
authors.initials: L
authors.lastname: Quintanilla-Martinez
authors.affiliation: Max Planck Institute for Intelligent Systems, Tuebingen, Germany.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
authors.affiliation: Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
authors.forename: Bernd J
authors.initials: BJ
authors.lastname: Pichler
authors.affiliation: Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
authors.forename: Jonathan A
authors.initials: JA
authors.lastname: Disselhorst
mesh_terms: D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000378:metabolism; D016000:Cluster Analysis; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008819:Mice, Nude; D009369:Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor
keywords: Fuzzy C-means; Gaussian mixture modeling; K-means; Multiparametric MRI; Spectral clustering; Tumor heterogeneity