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
Literatur
1
Kaneko M, Eguchi K, Ohmatsu H. et al .
Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography.
Radiology.
1996;
201
798-802
2
Sone S, Takashima S, Li F. et al .
Mass screening for lung cancer with mobile spiral computed tomography scanner.
Lancet.
1998;
351
1242-1245
3
Henschke C I, McCauley D I, Yankelevitz D F. et al .
Early Lung Cancer Action Project: overall design and findings from baseline screening.
Lancet.
1999;
354
99-105
4
Diederich S, Wormanns D, Semik M. et al .
Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers.
Radiology.
2002;
222
773-781
5
Swensen S J, Jett J R, Sloan J A. et al .
Screening for lung cancer with low-dose spiral computed tomography.
Am J Respir Crit Care Med.
2002;
165
508-513
6
Pastorino U, Bellomi M, Landoni C. et al .
Early lung-cancer detection with spiral CT and positron emission tomography in heavy smokers: 2-year results.
Lancet.
2003;
362
593-597
7
Diederich S, Wormanns D, Heindel W.
Radiologisches Screening des Bronchialkarzinoms: Aktueller Stand und zukünftige Perspektiven.
Fortschr Röntgenstr.
2001;
173
873-882
8
Novak C L, Shen H, Odry B. et al .
Performance of an automatic system for nodule correspondence in follow-up CT studies of the lung.
Radiology.
2002;
225 (p)
476
9
Wormanns D, Kohl G, Klotz E. et al .
Volumetric measurement of pulmonary nodules at multi-row detector CT: In vivo reproducibility.
Eur Radiol.
2004;
14
86-92
10
Yankelevitz D F, Gupta R, Zhao B. et al .
Small pulmonary nodules: evaluation with repeat CT-preliminary experience.
Radiology.
1999;
212
561-566
11
Eibel R, Turk T, Kulinna C. et al .
Wertigkeit multiplanarer Reformationen (MPR) in der Mehrschicht-Spiral-CT der Lunge.
Fortschr Röntgenstr.
2001;
173
57-64
12
Fischbach F, Knollmann F, Griesshaber V. et al .
Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness.
Eur Radiol.
2003;
13
2378-2383
13
Pauls S, Wahl J, Aschoff A J. et al .
EKG getriggerte Mehrzeilen- Spiral CT des Thorax zur Erkennung intrapulmonaler Metastasen.
Fortschr Röntgenstr.
2003;
175
640-645
14
Keogan M T, Tung K T, Kaplan D K. et al .
The significance of pulmonary nodules detected on CT staging for lung cancer.
Clin Radiol.
1993;
48
94-96
15
Benjamin M S, Drucker E A, McLoud T C. et al .
Small pulmonary nodules: detection at chest CT and outcome.
Radiology.
2003;
226
489-493
16
Hazelrigg S R, Boley T M, Weber D. et al .
Incidence of lung nodules found in patients undergoing lung volume reduction.
Ann Thorac Surg.
1997;
64
303-306
17
Furuya K, Murayama S, Soeda H. et al .
New classification of small pulmonary nodules by margin characteristics on high-resolution CT.
Acta Radiol.
1999;
40
496-504
18
Swensen S J, Viggiano R W, Midthun D E. et al .
Lung nodule enhancement at CT: multicenter study.
Radiology.
2000;
214
73-80
19
Yankelevitz D F, Reeves A P, Kostis W J. et al .
Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation.
Radiology.
2000;
217
251-256
20
Takashima S, Sone S, Li F. et al .
Indeterminate solitary pulmonary nodules revealed at population-based CT screening of the lung: using first follow-up diagnostic CT to differentiate benign and malignant lesions.
Am J Roentgenol.
2003;
180
1255-1263
21
Achenbach T, Vomweg T, Heussel C P. et al .
Computerunterstützte Diagnostik in der Thoraxradiologie- aktuelle Schwerpunkte und Techniken.
Fortschr Röntgenstr.
2003;
175
1471-1481
Dr. med. Florian Beyer
Institut für Klinische Radiologie, Universitätsklinikum Münster
Albert-Schweitzer-Straße 33
48129 Münster
Telefon: ++ 49/2 51/83 47-3 10
Fax: ++ 49/2 51/83 47-3 12
eMail: beyerf@uni-muenster.de