title: FDG-PET-based differential uptake volume histograms: a possible approach towards definition of biological target volumes.
abstract: OBJECTIVE
  Integration of fluorine-18 fludeoxyglucose ((18)F-FDG)-positron emission tomography (PET) functional data into conventional anatomically based gross tumour volume delineation may lead to optimization of dose to biological target volumes (BTV) in radiotherapy. We describe a method for defining tumour subvolumes using (18)F-FDG-PET data, based on the decomposition of differential uptake volume histograms (dUVHs).
  
  
  METHODS
  For 27 patients with histopathologically proven non-small-cell lung carcinoma (NSCLC), background uptake values were sampled within the healthy lung contralateral to a tumour in those image slices containing tumour and then scaled by the ratio of mass densities between the healthy lung and tumour. Signal-to-background (S/B) uptake values within volumes of interest encompassing the tumour were used to reconstruct the dUVHs. These were subsequently decomposed into the minimum number of analytical functions (in the form of differential uptake values as a function of S/B) that yielded acceptable net fits, as assessed by χ(2) values.
  
  
  RESULTS
  Six subvolumes consistently emerged from the fitted dUVHs over the sampled volume of interest on PET images. Based on the assumption that each function used to decompose the dUVH may correspond to a single subvolume, the intersection between the two adjacent functions could be interpreted as a threshold value that differentiates them. Assuming that the first two subvolumes spread over the tumour boundary, we concentrated on four subvolumes with the highest uptake values, and their S/B thresholds [mean ± standard deviation (SD)] were 2.88 ± 0.98, 4.05 ± 1.55, 5.48 ± 2.06 and 7.34 ± 2.89 for adenocarcinoma, 3.01 ± 0.71, 4.40 ± 0.91, 5.99 ± 1.31 and 8.17 ± 2.42 for large-cell carcinoma and 4.54 ± 2.11, 6.46 ± 2.43, 8.87 ± 5.37 and 12.11 ± 7.28 for squamous cell carcinoma, respectively.
  
  
  CONCLUSIONS
  (18)F-FDG-based PET data may potentially be used to identify BTV within the tumour in patients with NSCLC. Using the one-way analysis of variance statistical tests, we found a significant difference among all threshold levels among adenocarcinomas, large-cell carcinoma and squamous cell carcinomas. On the other hand, the observed significant variability in threshold values throughout the patient cohort (expressed as large SDs) can be explained as a consequence of differences in the physiological status of the tumour volume for each patient at the time of the PET/CT scan. This further suggests that patient-specific threshold values for the definition of BTVs could be determined by creation and curve fitting of dUVHs on a patient-by-patient basis.
  
  
  CONCLUSIONS
  The method of (18)F-FDG-PET-based dUVH decomposition described in this work may lead to BTV segmentation in tumours.
authors.affiliation: 1 Department of Radiation Oncology, Jewish General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Slobodan
authors.initials: S
authors.lastname: Devic
authors.affiliation: 1 Department of Radiation Oncology, Jewish General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Huriyyah
authors.initials: H
authors.lastname: Mohammed
authors.affiliation: 1 Department of Radiation Oncology, Jewish General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Nada
authors.initials: N
authors.lastname: Tomic
authors.affiliation: 1 Department of Radiation Oncology, Jewish General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Saad
authors.initials: S
authors.lastname: Aldelaijan
authors.affiliation: 1 Department of Radiation Oncology, Jewish General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: François
authors.initials: F
authors.lastname: De Blois
authors.affiliation: 2 Department of Radiation Oncology, Montreal General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Jan
authors.initials: J
authors.lastname: Seuntjens
authors.affiliation: 2 Department of Radiation Oncology, Montreal General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Shirley
authors.initials: S
authors.lastname: Lehnert
authors.affiliation: 2 Department of Radiation Oncology, Montreal General Hospital, McGill University, Montréal, QC, Canada.
authors.forename: Sergio
authors.initials: S
authors.lastname: Faria
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000378:metabolism*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019788:Fluorodeoxyglucose F18 / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism*; D008954:Models, Biological; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D047368:Tumor Burden
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18