title: Deriving global quantitative tumor response parameters from 18F-FDG PET-CT scans in patients with non-Hodgkin's lymphoma.
abstract: OBJECTIVE
  The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin's lymphoma (NHL) progression or response severity.
  
  
  METHODS
  A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis.
  
  
  RESULTS
  The set of proposed indicators achieved Pearson's correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUV mean, SUV max, SUV peak), and total lesion glycolysis] by more than 40%.
  
  
  CONCLUSIONS
  Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios.
authors.affiliation: aDepartment of Radiology, Faculty of Medicine bComputer Vision Center, Building O, Autonomous University of Barcelona cDepartment of Nuclear Medicine, Hospital de Sant Pau dDepartment of Applied Mathematics, Faculty of Mathematics, University of Barcelona, Barcelona, Spain.
authors.forename: Frederic
authors.initials: F
authors.lastname: Sampedro
authors.forename: Anna
authors.initials: A
authors.lastname: Domenech
authors.forename: Sergio
authors.initials: S
authors.lastname: Escalera
authors.forename: Ignasi
authors.initials: I
authors.lastname: Carrió
mesh_terms: D018450:Disease Progression; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008228:Lymphoma, Non-Hodgkin / Q000000981:diagnostic imaging* / Q000473:pathology; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden
chemical_list: D019788:Fluorodeoxyglucose F18