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Computer aided characterization aims to support the differential diagnosis of indeterminate pulmonary nodules. A
number of published studies have correlated automatically computed features from image processing with clinical
diagnoses of malignancy vs. benignity. Often, however, a high number of features was trained on a relatively small
number of diagnosed nodules, raising a certain skepticism as to how salient and numerically robust the various features
really are. On the way towards computer aided diagnosis which is trusted in clinical practice, the credibility of the
individual numerical features has to be carefully established.
Nodule volume is the most crucial parameter for nodule characterization, and a number of studies are testing its
repeatability. Apart from functional parameters (such as dynamic CT enhancement and PET uptake values), the next
most widely used parameter is the surface characteristic (vascularization, spicularity, lobulation, smoothness). In this
study, we test the repeatability of two simple surface smoothness features which can discriminate between smoothly
delineated nodules and those with a high degree of surface irregularity.
Robustness of the completely automatically computed features was tested with respect to the following aspects: (a)
repeated CT scan of the same patient with equal dose, (b) repeated CT scan with much lower dose and much higher
noise, (c) repeated automatic segmentation of the nodules using varying segmentation parameters, resulting in differing
nodule surfaces. The tested nodules (81) were all solid or partially solid and included a high number of sub- and juxtapleural
nodules. We found that both tested surface characterization features correlated reasonably well with each other
(80%), and that in particular the mean-surface-shape-index showed an excellent repeatability: 98% correlation between
equal dose CT scans, 93% between standard-dose and low-dose scan (without systematic shift), and 97% between
varying HU-threshold of the automatic segmentation, which makes it a reliable feature to be used in computer aided
diagnosis.
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