title: Inferring Stable Acquisition Durations for Applications of Perfusion Imaging in Oncology.
abstract: Tissue perfusion plays a critical role in oncology. Growth and migration of cancerous cells requires proliferation of networks of new blood vessels through the process of tumor angiogenesis. Many imaging technologies developed recently attempt to measure characteristics pertaining to the passage of fluid through blood vessels, thereby providing a noninvasive means for cancer detection, as well as treatment prognostication, prediction, and monitoring. However, because these techniques require a sequence of successive imaging scans under administration of intravenous imaging tracers, the quality of the resulting perfusion data depends on the acquisition protocol. In this paper, we explain how to infer stability for stochastic curve estimation. The topic is motivated by two recent attempts to determine stable acquisition durations for acquiring perfusion characteristics using dynamic computed tomography, wherein inference used inappropriate statistical methods. Notably, when appropriate statistical techniques are used, the resulting conclusions deviate substantially from those previously reported in the literature.
authors.affiliation: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Brian P
authors.initials: BP
authors.lastname: Hobbs
authors.affiliation: Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Chaan S
authors.initials: CS
authors.lastname: Ng
keywords: dynamic computed tomography; equivalence testing; penalized splines; perfusion imaging; semiparametric regression