A gel tumour phantom for assessment of the accuracy of manual and automatic delineation of gross tumour volume from FDG-PET/CT.
BACKGROUND
Our primary aim was to make a phantom for PET that could mimic a highly irregular tumour and provide true tumour contours. The secondary aim was to use the phantom to assess the accuracy of different methods for delineation of tumour volume from the PET images.


METHODS
An empty mould was produced on the basis of a contrast enhanced computed tomography (CT) study of a patient with a squamous cell carcinoma in the head and neck region. The mould was filled with a homogeneous fast-settling gel that contained both (18)F for positron emission tomography (PET) and an iodine contrast agent. This phantom (mould and gel) was scanned on a PET/CT scanner. A series of reference tumour contours were obtained from the CT images in the PET/CT. Tumour delineation based on the PET images was achieved manually, by isoSUV thresholding, and by a recently developed three-dimensional (3D) Difference of Gaussians algorithm (DoG). Average distances between the PET-derived and reference contours were assessed by a 3D distance transform.


RESULTS
The manual, thresholding and DoG delineation methods resulted in volumes that were 146%, 86% and 100% of the reference volume, respectively, and average distance deviations from the reference surface were 1.57 mm, 1.48 mm and 1.0, mm, respectively.


CONCLUSIONS
Manual drawing as well as isoSUV determination of tumour contours in geometrically irregular tumours may be unreliable. The DoG method may contribute to more correct delineation of the tumour. Although the present phantom had a homogeneous distribution of activity, it may also provide useful knowledge in the case of inhomogeneous activity distributions.


CONCLUSIONS
The geometric irregular tumour phantom with its inherent reference contours was an important tool for testing of different delineation methods and for teaching delineation.
The Intervention Centre, Oslo University Hospital, Oslo, Norway.
Arne
A
Skretting
Jan F
JF
Evensen
Ayca M
AM
Løndalen
Trond V
TV
Bogsrud
Otto K
OK
Glomset
Karsten
K
Eilertsen
D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy; D019788:Fluorodeoxyglucose F18; D005782:Gels; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008953:Models, Anatomic; D008954:Models, Biological; D064847:Multimodal Imaging / Q000379:methods*; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000532:radiotherapy; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography; D011880:Radiotherapy Planning, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D000077195:Squamous Cell Carcinoma of Head and Neck; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden / Q000502:physiology
D005782:Gels; D019788:Fluorodeoxyglucose F18