title: Defining a radiotherapy target with positron emission tomography.
abstract: OBJECTIVE
  F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is now considered the most accurate clinical staging study for non-small-cell lung cancer (NSCLC) and is also important in the staging of multiple other malignancies. Gross tumor volume (GTV) definition for radiotherapy, however, is typically based entirely on computed tomographic data. We performed a series of phantom studies to determine an accurate and uniformly applicable method for defining a GTV with FDG-PET.
  
  
  METHODS
  A model-based method was tested by a phantom study to determine a threshold, or unique cutoff of standardized uptake value based on body weight (standardized uptake value [SUV]) for FDG-PET based GTV definition. The degree to which mean target SUV, background FDG concentration, and target volume influenced that GTV definition were evaluated. A phantom was constructed consisting of a 9.0-L cylindrical tank. Glass spheres with volumes ranging from 12.2 to 291.0 cc were suspended within the tank, with a minimum separation of 4 cm between the edges of the spheres. The sphere volumes were selected based on the range of NSCLC patient tumor volumes seen in our clinic. The tank and spheres were filled with a variety of known concentrations of FDG in several experiments and then scanned using a General Electric Advance PET scanner. In the initial experiment, six spheres with identical volumes were filled with varying concentrations of FDG (mean SUV = 1.85 approximately 9.68) and suspended within a background bath of FDG at a similar concentration to that used in clinical practice (0.144 muCi/mL). The second experiment was identical to the first, but was performed at 0.144 and 0.036 muCi/mL background concentrations to determine the effect of background FDG concentration on sphere definition. In the third experiment, six spheres with volumes of 12.2 to 291.0 cc were filled with equal concentrations of FDG and suspended in a standard background FDG concentration of 0.144 muCi/mL. Sphere images in each experiment were auto-contoured (simulating a GTV) using the threshold SUV that yielded a volume matching that of the known sphere volume. A regressive function was constructed to represent the relationship between the threshold SUV and the mean target SUV. This function was then applied to define the GTV of 15 NSCLC patients. The GTV volumes were compared to those determined by a fixed image intensity threshold proposed by other investigators.
  
  
  RESULTS
  There was a strong linear relationship between the threshold SUV and the mean target SUV. The linear regressive function derived was: threshold SUV = 0.307 x (mean target SUV) + 0.588. The background concentration and target volume indirectly affect the threshold SUV by way of their influence on the mean target SUV. We applied the linear regressive function, as well as a fixed image intensity threshold (42% of maximum intensity) to the sphere phantoms and 15 patients with NSCLC. The results indicated that a much smaller deviation occurred when the threshold SUV regressive function was utilized to estimate the phantom volume as compared to the fixed image intensity threshold. The average absolute difference between the two methods was 21% with respect to the true phantom volume. The deviation became even more pronounced when applied to true patient GTV volumes, with a mean difference between the two methods of 67%. This was largely due to a greater degree of heterogeneity in the SUV of tumors over phantoms.
  
  
  CONCLUSIONS
  An FDG-PET-based GTV can be systematically defined using a threshold SUV according to the regressive function described above. The threshold SUV for defining the target is strongly dependent on the mean target SUV of the target, and can be uniquely determined through the proposed iteration process.
authors.affiliation: 21st Century Oncology, Inc., Asheville, NC, USA.
authors.forename: Quinten C
authors.initials: QC
authors.lastname: Black
authors.forename: Inga S
authors.initials: IS
authors.lastname: Grills
authors.forename: Larry L
authors.initials: LL
authors.lastname: Kestin
authors.forename: Ching-Yee O
authors.initials: CY
authors.lastname: Wong
authors.forename: John W
authors.initials: JW
authors.lastname: Wong
authors.forename: Alvaro A
authors.initials: AA
authors.lastname: Martinez
authors.forename: Di
authors.initials: D
authors.lastname: Yan
mesh_terms: D001835:Body Weight; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging; D019788:Fluorodeoxyglucose F18 / Q000493:pharmacokinetics; D016014:Linear Models; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011859:Radiography; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18