This paper presents a general framework for assessing imaging systems and image-analysis methods on the basis
of therapeutic rather than diagnostic efficacy. By analogy to receiver operating characteristic (ROC) curves,
it introduces the Therapy Operating Characteristic or TOC curve, which is a plot of the probability of tumor
control vs. the probability of normal-tissue complications as the overall level of a radiotherapy treatment beam
is varied. The proposed figure of merit is the area under the TOC, denoted AUTOC. If the treatment planning
algorithm is held constant, AUTOC is a metric for the imaging and image-analysis components, and in particular
for segmentation algorithms that are used to delineate tumors and normal tissues. On the other hand, for a
given set of segmented images, AUTOC can also be used as a metric for the treatment plan itself. A general
mathematical theory of TOC and AUTOC is presented and then specialized to segmentation problems. Practical
approaches to implementation of the theory in both simulation and clinical studies are presented. The method
is illustrated with a a brief study of segmentation methods for prostate cancer.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.