Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach.
In this study, we described and validated an unsupervised segmentation algorithm for the assessment of tumor heterogeneity using dynamic18F-FDG PET. The aim of our study was to objectively evaluate the proposed method and make comparisons with compartmental modeling parametric maps and SUV segmentations using simulations of clinically relevant tumor tissue types.Methods:An irreversible 2-tissue-compartmental model was implemented to simulate clinical and preclinical18F-FDG PET time-activity curves using population-based arterial input functions (80 clinical and 12 preclinical) and the kinetic parameter values of 3 tumor tissue types. The simulated time-activity curves were corrupted with different levels of noise and used to calculate the tissue-type misclassification errors of spectral clustering (SC), parametric maps, and SUV segmentation. The utility of the inverse noise variance- and Laplacian score-derived frame weighting schemes before SC was also investigated. Finally, the SC scheme with the best results was tested on a dynamic18F-FDG measurement of a mouse bearing subcutaneous colon cancer and validated using histology.Results:In the preclinical setup, the inverse noise variance-weighted SC exhibited the lowest misclassification errors (8.09%-28.53%) at all noise levels in contrast to the Laplacian score-weighted SC (16.12%-31.23%), unweighted SC (25.73%-40.03%), parametric maps (28.02%-61.45%), and SUV (45.49%-45.63%) segmentation. The classification efficacy of both weighted SC schemes in the clinical case was comparable to the unweighted SC. When applied to the dynamic18F-FDG measurement of colon cancer, the proposed algorithm accurately identified densely vascularized regions from the rest of the tumor. In addition, the segmented regions and clusterwise average time-activity curves showed excellent correlation with the tumor histology.Conclusion:The promising results of SC mark its position as a robust tool for quantification of tumor heterogeneity using dynamic PET studies. Because SC tumor segmentation is based on the intrinsic structure of the underlying data, it can be easily applied to other cancer types as well.
Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.|Max Planck Institute for Intelligent Systems, Tuebingen, Germany; and.
Prateek
P
Katiyar
Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
Mathew R
MR
Divine
Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany.
Ursula
U
Kohlhofer
Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany.
Leticia
L
Quintanilla-Martinez
Max Planck Institute for Intelligent Systems, Tuebingen, Germany; and.
Bernhard
B
Schölkopf
Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
Bernd J
BJ
Pichler
Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany Jonathan.Disselhorst@med.uni-tuebingen.de.
Jonathan A
JA
Disselhorst
D000818:Animals; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007700:Kinetics; D051379:Mice; D008954:Models, Biological; D009373:Neoplasms, Germ Cell and Embryonal / Q000000981:diagnostic imaging* / Q000473:pathology*; D049268:Positron-Emission Tomography; D059629:Signal-To-Noise Ratio
D019788:Fluorodeoxyglucose F18
18F-FDG PET; SUV; compartmental modeling; spectral clustering; tumor heterogeneity