Multi-level otsu method to define metabolic tumor volume in positron emission tomography.
Year: 2018
Type: article
Abstract: This study was to validate reliability and clinical utility of a PET tumor segmentation method using multi-level Otsu (MO-PET) in standard National Electrical Manufacturers Association (NEMA) image quality (IQ) phantom and patients with osteosarcoma. The NEMA IQ phantom was prepared with a lesion-to-background ratio (LBR) of either 8:1, 4:1, or 1.5:1. The artificial lesions in the phantom were segmented using MO-PET, gradient-based method (PETedge), relative threshold methods, and background threshold methods. Metabolic tumor volumes (MTVs) using MO-PET and PETedge were named as MTV (MO-PET) and MTV (PETedge), respectively. Among the MTVs using multiple methods, only MTV (MO-PET) and MTV (PETedge) showed excellent agreements with the actual volume of NEMA IQ phantom across the different LBRs (intraclass correlation coefficient, ICC = 0.987, 0.985 in LBR 8:1, 0.981, 0.993 in LBR 4:1 and 0.947, 0.994 in LBR 1.5:1). Repeated measurements of MTV (MO-PET) of the primary tumors showed excellent reproducibility with ICC of 0.994 (0.989-0.997) in patients with osteosarcoma. Also, MTV (MO-PET) was found to be predictive of Event Free Survival (EFS) [Hazard ratio (95% CI) = 6.1 (2.1-17.2), log rank P = 0.0003] in patients with osteosarcoma. We have validated in NEMA IQ phantom that the MTV (MO-PET) is accurate, and importantly, stable and consistent across a range of lesion sizes and LBRs representative of clinical tumor lesions. Furthermore, MTV (MO-PET) showed excellent reproducibility and was predictive for EFS in patients with osteosarcoma.
Source: Europe PMC (PubMed Central)
Institutions: University of Wisconsin–Madison, Seoul National University, Johns Hopkins University, Johns Hopkins Medicine, Korea Institute of Radiological and Medical Sciences, The University of Texas MD Anderson Cancer Center, University of Southern California, Children's Hospital Los Angeles, St. Jude Children's Research Hospital, University of Wisconsin Carbone Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2990799972