Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images.
OBJECTIVE
A co-registered multimodality pattern analysis segmentation system (COMPASS) was developed to automatically delineate the radiation targets in head-and-neck cancer (HNC) using both (18)F-fluoro-deoxy glucose-positron emission tomography (PET) and computed tomography (CT) images. The performance of the COMPASS was compared with the results of existing threshold-based methods and radiation oncologist-drawn contours.


METHODS
The COMPASS extracted texture features from corresponding PET and CT voxels. Using these texture features, a decision-tree-based K-nearest-neighbor classifier labeled each voxel as either "normal" or "abnormal." The COMPASS was applied to the PET/CT images of 10 HNC patients. Automated segmentation results were validated against the manual segmentations of three radiation oncologists using the volume, sensitivity, and specificity. The performance of the COMPASS was compared with three PET-based threshold methods: standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio.


RESULTS
The tumor delineations of the COMPASS were both quantitatively and qualitatively more similar to those of the radiation oncologists than the delineations from the other methods. The specificity was 95% +/- 2%, 84% +/- 9%, 98% +/- 3%, and 96% +/- 4%, and the sensitivity was 90% +/- 12%, 93% +/- 10%, 48% +/- 20%, and 68% +/- 25% for the COMPASS, for a standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio, respectively. The COMPASS distinguished HNC from adjacent normal tissues with high physiologic uptake and consistently defined tumors with large variability in (18)F-fluoro-deoxy glucose uptake, which are often problematic with the threshold-based methods.


CONCLUSIONS
Automated segmentation using texture analysis of PET/CT images has the potential to provide accurate delineation of HNC. This could lead to reduced interobserver variability, reduced uncertainty in target delineation, and improved treatment planning accuracy.
Yu
Huan
H
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
Caldwell
Curtis
C
Mah
Katherine
K
Poon
Ian
I
Balogh
Judith
J
MacKenzie
Robert
R
Khaouam
Nader
N
Tirona
Romeo
R
D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D019788:Fluorodeoxyglucose F18; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D006801:Humans; D008297:Male; D008875:Middle Aged; D009303:Nasopharyngeal Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D009959:Oropharyngeal Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D049268:Positron-Emission Tomography / Q000379:methods*; D011446:Prospective Studies; D019275:Radiopharmaceuticals; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014062:Tongue Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D014067:Tonsillar Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18