NEW YORK (Reuters Health), Oct 13 - In staging melanoma patients with palpable lymph nodes, fluorine-18 (F-18) FDG-PET and CT are equivalent, but FDG-PET detects more metastatic sites, particularly bone and subcutaneous metastases, Dutch researchers report in the October 1 issue of the Journal of Clinical Oncology.
Dr. Harald J. Hoekstra of University Medical Centre Groningen and colleagues came to these conclusions after prospectively studying data on 251 patients who underwent both FDG-PET and CT at five centers.
Distant metastases were suggested by FDG-PET in 32% of the patients and by CT in 29%. After correlation with cytology and histology or six months of follow-up, results proved correct in 27% of FDG-PET scans and 24% of CT scans.
The article notes that results were correct in 68 of 79 patients (86%) with positive FDG-PET scans and in 61 of 72 (85%) with positive CT scans.
False-positive rates were not statistically different between hospitals, according to the researchers.
Significantly more sites were detected with FDG-PET than with CT (133 versus 112). This was particularly the case for bone metastases (27 versus 10) and subcutaneous metastases (11 versus five).
Treatment changed in 19% of the patients. In most of these cases (79%), therapy was changed on the basis of both scans. Changes were made solely a result of FDG-PET in 17%, and solely a result of CT in 4%.
The researchers also calculated that FDG-PET provided value in addition to that of spiral CT in 17% of patients. Conversely, CT was of additional value in 9% of the patients.
"Due to the improved staging of melanoma patients with lymph node metastases," Dr. Hoekstra told Reuters Health, "surgical oncologists can better select melanoma patients for curative therapeutic lymph node dissection and refer patients with distant disease to a medical oncologist for systemic treatment."
Overall, he concluded, based on FDG-PET, "melanoma patients with lymph node metastases get the best tailored treatment."
By David Douglas
J Clin Oncol 2009;27:4774-4780.
Last Updated: 2009-10-12 18:00:22 -0400 (Reuters Health)
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
