
NEW YORK (Reuters Health), Dec 25 - Positron emission tomography (PET) following infusion of F-18 fluorodeoxyglucose (FDG) is an effective means of predicting response to neoadjuvant chemotherapy in patients with breast cancer, French researchers report in the December 1st issue of the Journal of Clinical Oncology.
Dr. Caroline Rousseau of Centre Rene Gauducheau, Saint Herblain, and colleagues note that changes in tumor metabolism take place before changes in tumor size. Studies have suggested that FDG uptake may reflect these changes sooner than is possible with other methods.
To investigate, the researchers studied stage II and II breast cancer patients who were receiving neoadjuvant chemotherapy. After administration of FDG, PET images were taken at baseline, and after the first, second, third, and sixth course of chemotherapy.
Surgery was performed after the sixth course and gross residual disease was seen in 28 patients. FDG standardized uptake values did not vary much in this group.
However, in 34 of the remaining 36 patients with minimal residual disease, values decreased markedly to background levels.
Using 60% of the baseline standardized uptake value as a cutoff point, the sensitivity of the approach was 61% after one course of chemotherapy, 89% after two, and 88% after three. Corresponding values for specificity were 96%, 95%, and 73% and for negative predictive value, 68%, 85%, and 83%.
This was considerable better than the results achieved using ultrasound or ultrasonography. In fact, assessment of tumor response using either of these modalities did reach significance regardless of the cutoff point chosen.
The researchers call for further studies, but observe that the approach can provide useful information on response as early as the second course of chemotherapy.
"Early information about tumor response," they conclude, "is extremely helpful in deciding the most appropriate therapeutic strategy."
Last Updated: 2006-12-22 13:00:13 -0400 (Reuters Health)
J Clin Oncol 2006;24:5366-5372.
Related Reading
Whole-body FDG-PET of little use in breast cancer staging, November 26, 2006
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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&fit=crop&h=112&q=70&w=112)






