An algorithm for automatic analysis of portal images: clinical evaluation for prostate treatments.
The aim of this study is to assess the clinical value of an algorithm for automatic analysis of portal images by measuring the method's performance in a clinical study of treatment of prostate cancer. The algorithm is based on chamfer matching and measures displacements of patients relative to prescribed radiation beam positions. In this paper we propose a method to quantify the mean standard deviation (MSD) of the performance of automatic analysis relative to the MSD of the performance of trained radiographers using the clinical data set only, i.e. without using additional phantoms or simulations. The clinical data set in this study consists of 99 regional AP prostate images of 15 different patients. To assess the performance the automatic analysis in relation to that of the human observers, we studied the results of the unsupervised automatic analysis, as well as the results of a less-trained human observer and a well-trained human observer assisted by the automatic analysis (in this combination, automatic analysis is done first and the result is modified by the well-trained observer if the observer does not agree). First, the intra-observer variations of the well-trained observer are measured by repetitive analysis of a small subset of the clinical data. The distribution of differences in analysis between two arbitrary observers is described by the chi 2 distribution, and is tabulated in literature. We define the agreement histogram of an observer O as an estimator for the chi 2 distribution between O and the well-trained human observer, parameterized by the ratio of the intra-observer variations of O and the well-trained observer.(ABSTRACT TRUNCATED AT 250 WORDS)
Gilhuijs
K G
KG
Radiotherapy Department, The Netherlands Cancer Institute Antoni van Leeuwenhoek Huis, Amsterdam.
el-Gayed
A A
AA
van Herk
M
M
Vijlbrief
R E
RE
D000465:Algorithms; D006801:Humans; D008297:Male; D015588:Observer Variation; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted