Zusammenfassung
Ziel: Klinische Prüfung eines Softwarealgorithmus, der bei der Suche korrespondierender
Lungenrundherde in CT-Verlaufskontrollen Unterstützung bieten soll, und Identifizierung
der Faktoren, die die Rate korrekt lokalisierter Herde beeinflussen. Methode: 11 Patienten mit 22 Mehrdetektor-Spiral-CT-Untersuchungen des Thorax (Siemens Somatom
VZ; Röhrenspannung 120 kVp; effektiver Röhrenstrom 20 oder 100 mAs; Kollimation 4
× 1 mm; Rekonstruktionsinkrement 0,8 mm) mit insgesamt 190 Rundherden wurden mit dem
sog. „real-time automatic matching” (RAM-)Algorithmus (Siemens LungCare) analysiert.
Durchmesser, Randschärfe (scharf/unscharf) und Lokalisation (Lungenoberfeld/-mittelfeld/-unterfeld;
zentral/peripher; rechts/links) der Herde sowie die Inspirationstiefe (identisch/>
5 % unterschiedlich) wurden aufgezeichnet. Die Rate automatisch korrekt lokalisierter
Herde wurde mit diesen Parametern mittels χ2 -Test verglichen. Ergebnisse: Der RAM-Algorithmus war in der Lage, 164 der 190 korrespondierenden Lungenrundherde
(86,3 %) korrekt zu lokalisieren. Die Detektionsrate war dabei nicht von der Herdlokalisation
oder dem Herddurchmesser abhängig. Der Einfluss der Inspirationstiefe war hingegen
hochsignifikant (p < 0,001): Bei gleicher Inspirationslage lag die Detektionsrate
bei 100 % (146/146), bei unterschiedlichen Atemlagen bei 40,9 % (18/44). Die Beobachtung
einer signifikant besseren Detektion unscharf begrenzter Herde (p = 0,028) entspricht
einem statistischen Artefakt. Schlussfolgerung: Der RAM-Algorithmus erwies sich als zuverlässige Hilfe zum Auffinden korrespondierender
Lungenrundherde in CT-Verlaufskontrollen. Limitierend sind stark differierende Atemlagen.
Abstract
Purpose: 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. Materials and Methods: The “real-time automatic matching” (RAM) algorithm (Siemens LungCare™ 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); 4x1 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. χ²-test was used to describe influence of nodule features on detection rate.
Influence of nodule size was assessed using Mann-Whitney-U-Test. Results: 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. Conclusion: RAM is a valuable tool for follow-up of lung nodules at CT. Only very different inspiration
levels influenced detection rate.
Key words
Computed tomography (CT) - lung nodule - computers, diagnostic aid - lung neoplasms,
diagnosis
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Dr. med. Florian Beyer
Institut für Klinische Radiologie, Universitätsklinikum Münster
Albert-Schweitzer-Straße 33
48129 Münster
Phone: ++ 49/2 51/83 47-3 10
Fax: ++ 49/2 51/83 47-3 12
Email: beyerf@uni-muenster.de