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DOI: 10.1055/s-0038-1625207
Comparison of manual and automated quantification methods of 123I-ADAM
123I-ADAM: Vergleich manueller und automatischer MethodenPublication History
Received:
03 January 2005
in revised form:
21 March 2005
Publication Date:
11 January 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00034924/200505/lookinside/thumbnails/10-1055-s-0038-1625207_de-1.jpg)
Summary:
123I-ADAM is a novel radioligand for imaging of the brain serotonin transporters (SERTs). Traditionally, the analysis of brain receptor studies has been based on observer-dependent manual region of interest definitions and visual interpretation. Our aim was to create a template for automated image registrations and volume of interest (VOI) quantifications and to show that an automated quantification method of 123I-ADAM is more repeatable than the manual method. Patients, methods: A template and a predefined VOI map was created from 123I-ADAM scans done for healthy volunteers (n = 15). Scans of another group of healthy persons (HS, n = 12) and patients with bulimia nervosa (BN, n = 10) were automatically fitted to the template and specific binding ratios (SBRs) were calculated by using the VOI map. Manual VOI definitions were done for the HS and BN groups by both one and two observers. The repeatability of the automated method was evaluated by using the BN group. Results: For the manual method, the interobserver coefficient of repeatability was 0.61 for the HS group and 1.00 for the BN group. The intraobserver coefficient of repeatability for the BN group was 0.70. For the automated method, the coefficient of repeatability was 0.13 for SBRs in midbrain. Conclusion: An automated quantification gives valuable information in addition to visual interpretation decreasing also the total image handling time and giving clear advantages for research work. An automated method for analysing 123I-ADAM binding to the brain SERT gives repeatable results for fitting the studies to the template and for calculating SBRs, and could therefore replace manual methods.
Zusammenfassung:
123I-ADAM ist ein neuer Radioligand für die bildgebende Diagnostik der zerebralen Serotonintransporter (SERTs). Bisher beruhte die Analyse zerebraler Rezeptorstudien auf der Beobachter-abhängigen Definition der Bereiche besonderen Interesses und der visuellen Interpretation. Unser Ziel war, eine Schablone für automatische Bilddeckungen und für Quantifizierungen des interessierenden Volumens (VOI) zu erstellen und zu zeigen, dass eine automatische Methode zur Quantifizierung von 123I-ADAM reproduzierbarer ist als die manuelle Methode. Patienten, Methoden: Aus 123I-ADAM-Scans, die bei Gesunden durchgeführt wurden (n = 15), wurden eine Schablone und eine vordefinierte VOI-Karte erstellt. Scans einer anderen Gruppe Gesunder (GP, n = 12) und von Patienten mit Bulimia nervosa (BN, n = 10) wurden automatisch in die Schablone eingepasst, und es wurden spezielle Bindungsquotienten (specific binding ratios – SBRs) unter Verwendung der VOIKarte berechnet. Die manuellen VOI-Definitionen wurden für die GP- und BN-Gruppe sowohl von einem als auch von zwei Beobachtern vorgenommen. Die Reproduzierbarkeit der automatischen Methode wurde anhand der BN-Gruppe ausgewertet. Ergebnisse: Bei der manuellen Methode betrug der Interbeobachter-Koeffizient der Reproduzierbarkeit bei der GP-Gruppe 0,61 und bei der BN-Gruppe 1,00. Der Intrabeobachter-Koeffizient der Reproduzierbarkeit für die BN-Gruppe betrug 0,70. Bei der automatischen Methode betrug der Koeffizient der Reproduzierbarkeit für die SBRs im Mittelhirn 0,13. Schlussfolgerung: Eine automatische Quantifizierung liefert zusätzlich zur visuellen Interpretation wertvolle Informationen und verkürzt gleichzeitig die Gesamt-Bildbearbeitungszeit und bietet eindeutige Vorteile für wissenschaftliche Arbeiten. Eine automatische Methode für die Analyse der 123I-ADAM-Bindung an den zerebralen SERT liefert reproduzierbare Ergebnisse für die Einpassung der Studien in die Schablone und für die Berechnung der SBRs; sie könnte manuelle Methoden ersetzen.
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