Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy.
OBJECTIVE
The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment.


METHODS
We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT.


RESULTS
Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found.


CONCLUSIONS
The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.
Department of Bioengineering, Politecnico di Milano, Milano, Italy. marta.peroni@mail.polimi.it
Marta
M
Peroni
Delia
D
Ciardo
Maria Francesca
MF
Spadea
Marco
M
Riboldi
Stefania
S
Comi
Daniela
D
Alterio
Guido
G
Baroni
Roberto
R
Orecchia
D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003657:Decision Making; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D058958:Organs at Risk / Q000000981:diagnostic imaging* / Q000528:radiation effects; D010306:Parotid Gland / Q000000981:diagnostic imaging; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods; D012189:Retrospective Studies; D047368:Tumor Burden / Q000528:radiation effects