[Clinical evaluation of a software for automated localization of lung nodules at follow-up CT examinations]

To evaluate a software algorithm for automated localization of pulmonary nodules at follow-up CT examinations of the chest and to determine factors influencing the rate of correctly matched nodules.

The "real-time automatic matching" (RAM) algorithm (Siemens LungCare trade mark software) was applied to 22 follow-up multirow-detector CT (MDCT) examinations in 11 patients (Siemens Somatom VolumeZoom, tube voltage 120 kVp; effective tube current 20 mAs (n = 18) or 100 mAs (n = 4); 4 x 1 mm detector configuration, 1.25 mm slice thickness; 0.8 mm reconstruction increment; standard lung kernel B50f) with a total of 190 lung nodules (mean diameter 6.7 +/- 3.5 mm, range 2 - 17 mm). The following nodule features were recorded: diameter, edge definition (well- or ill-defined), location (upper, middle or lower third; central or peripheral; right or left lung) and inspiration level (considered identical if the difference of diaphragm-apex distance between baseline and follow-up examination was < 5 %, otherwise it was considered different). A nodule was regarded as correctly localized if the marking box drawn by the software was visible on at least one slice together with the nodule and the center of the nodule was located inside the marking box. chi(2)-test was used to describe influence of nodule features on detection rate. Influence of nodule size was assessed using Mann-Whitney-U-Test.

RAM correctly located 164 of 190 of all lung nodules (86.3 %). Detection rate did not depend on nodule location (left vs. right lung: p = 0.48; upper vs. middle vs. lower third: p = 0.96; peripheral vs. central: p = 0.47) or diameter (p = 0.30). Influence of inspiration level was highly significant (p < 0.001): nodules were detected in 100 % (146/146) for identical inspiration levels and in 40.9 % (18/44) for different inspiration levels. The observation of a significant better localization of ill-defined nodules (p = 0.028) corresponds to a statistical artifact due to the inhomogeneous distributions of this specific feature in our data.

RAM is a valuable tool for follow-up of lung nodules at CT. Only very different inspiration levels influenced detection rate.