Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome

18F-FDG PET images of tumors often display highly heterogeneous spatial distribution of 18F-FDG–positive pixels. We proposed that this heterogeneity in 18F-FDG spatial distribution can be used to predict tumor biologic aggressiveness. This study presents data to support the hypothesis that a new heterogeneity-analysis algorithm applied to 18F-FDG PET images of tumors in patients is predictive of patient outcome. Methods: 18F-FDG PET images from 238 patients with sarcoma were analyzed using a new algorithm for heterogeneity analysis in tumor 18F-FDG spatial distribution. Patient characteristics, tumor histology, and patient outcome were compared with image analysis results using univariate and multivariate analysis. Cox proportional hazards models were used to further analyze the significance of the data associations. Results: Statistical analyses show that heterogeneity analysis is a strong independent predictor of patient outcome. Conclusion: The new 18F-FDG PET tumor image heterogeneity analysis method is validated for the ability to predict patient outcome in a clinical population of patients with sarcoma. This method can be extended to other PET image datasets in which heterogeneity in tissue uptake of a radiotracer may predict patient outcome.

[1]  A. Cabrera,et al.  OSTEOSARCOMA OF BONE. , 1964, Surgery, gynecology & obstetrics.

[2]  R. Auerbach,et al.  Regional differences in the growth of normal and neoplastic cells. , 1982, Science.

[3]  K. Kinzler,et al.  Cancer-susceptibility genes. Gatekeepers and caretakers. , 1997, Nature.

[4]  F. Collin,et al.  Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma. , 1997, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  D. Mankoff,et al.  Tumor metabolic rates in sarcoma using FDG PET. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[6]  C Lengauer,et al.  Genetic instability and darwinian selection in tumours. , 1999, Trends in cell biology.

[7]  J. Eary,et al.  Positron Emission Tomography in Grading Soft Tissue Sarcomas. , 1999, Seminars in musculoskeletal radiology.

[8]  A. Folpe,et al.  (F-18) fluorodeoxyglucose positron emission tomography as a predictor of pathologic grade and other prognostic variables in bone and soft tissue sarcoma. , 2000, Clinical cancer research : an official journal of the American Association for Cancer Research.

[9]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[10]  Mina J. Bissell,et al.  Putting tumours in context , 2001, Nature Reviews Cancer.

[11]  F. O’Sullivan,et al.  Sarcoma tumor FDG uptake measured by PET and patient outcome: a retrospective analysis , 2002, European Journal of Nuclear Medicine and Molecular Imaging.

[12]  Finbarr O'Sullivan,et al.  A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. , 2003, Biostatistics.

[13]  K. Loeb,et al.  Multiple mutations and cancer , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  F O'Sullivan,et al.  Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. , 2005, Biostatistics.

[15]  Joel Karp,et al.  Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials. , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[16]  S. Thiagalingam,et al.  A cascade of modules of a network defines cancer progression. , 2006, Cancer research.