Literature DB >> 27175376

Therapy operating characteristic curves: tools for precision chemotherapy.

Harrison H Barrett1, David S Alberts2, James M Woolfenden3, Luca Caucci4, John W Hoppin5.   

Abstract

The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control versus the probability of normal-tissue complications as the overall radiation dose level is varied, e.g., by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy. This paper shows how TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy, AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. The mathematical analogy between response of observers to images and the response of tumors to distributions of a chemotherapy drug is exploited to obtain linear discriminant functions from which AUTOC can be calculated. Methods for using mathematical models of drug delivery and tumor response with imaging data to estimate patient-specific parameters that are needed for calculation of AUTOC are outlined. The implications of this viewpoint for clinical trials are discussed.

Entities:  

Keywords:  chemotherapy; normal tissue complications; positron emission tomography; radiation therapy; single-photon emission computed tomography; therapy operating characteristic; tumor control

Year:  2016        PMID: 27175376      PMCID: PMC4852214          DOI: 10.1117/1.JMI.3.2.023502

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  35 in total

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2.  Physiological random processes in precision cancer therapy.

Authors:  Nick Henscheid; Eric Clarkson; Kyle J Myers; Harrison H Barrett
Journal:  PLoS One       Date:  2018-06-29       Impact factor: 3.240

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