Radiation Oncology | |
Accuracy of deformable image registration for contour propagation in adaptive lung radiotherapy | |
Wolfgang A Tomé3  Karl Bzdusek4  Dirk De Ruysscher2  Wouter van Elmpt1  Nicholas Hardcastle5  | |
[1] Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands;Radiation Oncology, University Hospitals Leuven/ KU Leuven, Leuven, Belgium;Department of Radiation Oncology, Montefiore Medical Center and Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA;Philips Radiation Oncology Systems, 5520 Nobel Drive, 53711 Fitchburg, USA;Center for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia | |
关键词: Automatic contour propagation; NSCLC; Adaptive radiotherapy; Deformable image registration; | |
Others : 1152670 DOI : 10.1186/1748-717X-8-243 |
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received in 2013-07-10, accepted in 2013-09-28, 发布年份 2013 | |
【 摘 要 】
Background
Deformable image registration (DIR) is an attractive method for automatic propagation of regions of interest (ROIs) in adaptive lung radiotherapy. This study investigates DIR for automatic contour propagation in adaptive Non Small Cell Lung Carcinoma patients.
Methods
Pre and mid-treatment fan beam 4D-kVCT scans were taken for 17 NSCLC patients. Gross tumour volumes (GTV), nodal-GTVs, lungs, esophagus and spinal cord were delineated on all kVCT scans. ROIs were propagated from pre- to mid-treatment images using three DIR algorithms. DIR-propagated ROIs were compared with physician-drawn ROIs on the mid-treatment scan using the Dice score and the mean slicewise Hausdorff distance to agreement (MSHD). A physician scored the DIR-propagated ROIs based on clinical utility.
Results
Good agreement between the DIR-propagated and physician drawn ROIs was observed for the lungs and spinal cord. Agreement was not as good for the nodal-GTVs and esophagus, due to poor soft-tissue contrast surrounding these structures. 96% of OARs and 85% of target volumes were scored as requiring no or minor adjustments.
Conclusions
DIR has been shown to be a clinically useful method for automatic contour propagation in adaptive radiotherapy however thorough assessment of propagated ROIs by the treating physician is recommended.
【 授权许可】
2013 Hardcastle et al.; licensee BioMed Central Ltd.
【 预 览 】
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Figure 1. | 55KB | Image | download |
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【 参考文献 】
- [1]Belderbos JS, Heemsbergen WD, De Jaeger K, Baas P, Lebesque JV: Final results of a phase I/II dose escalation trial in non-small-cell lung cancer using three-dimensional conformal radiotherapy. Int J Radiat Oncol Biol Phys 2006, 66:126-134.
- [2]Kong FM, Hayman JA, Griffith KA, Kalemkerian GP, Arenberg D, Lyons S, Turrisi A, Lichter A, Fraass B, Eisbruch A, Lawrence TS, Ten Haken RK: Final toxicity results of a radiation-dose escalation study in patients with non-small-cell lung cancer (NSCLC): predictors for radiation pneumonitis and fibrosis. Int J Radiat Oncol Biol Phys 2006, 65:1075-1086.
- [3]van Baardwijk A, Wanders S, Boersma L, Borger J, Ollers M, Dingemans A, Bootsma G, Geraedts W, Pitz C, Lunde R, Lambin P, De Ruysscher D: Mature results of an individualized radiation dose prescription study based on normal tissue constraints in stages I to III non-small-cell lung cancer. J Clin Oncol 2010, 28:1380-1386.
- [4]Bradley JD, Moughan J, Graham MV, Byhardt R, Govindan R, Fowler J, Purdy JA, Michalski JM, Gore E, Choy H: A phase I/II radiation dose escalation study with concurrent chemotherapy for patients with inoperable stages I to III Non-small-cell lung cancer: phase I results of RTOG 0117. Int J Radiat Oncol Biol Phys 2010, 77:367-372.
- [5]Guckenberger M, Wilbert J, Richter A, Baier K, Flentje M: Potential of adaptive radiotherapy to escalate the radiation dose in combined radiochemotherapy for locally advanced non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2011, 79:901-908.
- [6]Ramsey CR, Langen KM, Kupelian PA, Scaperoth DD, Meeks SL, Mahan SL, Seibert RM: A technique for adaptive image-guided helical tomotherapy for lung cancer. Int J Radiat Oncol Biol Phys 2006, 64:1237-1244.
- [7]Guckenberger M, Baier K, Richter A, Wilbert J, Flentje M: Evolution of surface-based deformable image registration for adaptive radiotherapy of non-small cell lung cancer (NSCLC). Radiat Oncol 2009, 4:68. BioMed Central Full Text
- [8]Ehler ED, Bzdusek K, Tome WA: A method to automate the segmentation of the Gtv and Itv for lung tumors. Med Dosim 2009, 34:145-153.
- [9]Lu WG, Olivera GH, Chen Q, Chen ML, Ruchala KJ: Automatic re-contouring in 4D radiotherapy. Phys Med Biol 2006, 51:1077-1099.
- [10]Orban de Xivry J, Janssens G, Bosmans G, De Craene M, Dekker A, Buijsen J, van Baardwijk A, De Ruysscher D, Macq B, Lambin P: Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients. Radiother Oncol 2007, 85:232-238.
- [11]Shekhar R, Lei P, Castro-Pareja CR, Plishker WL, D'Souza WD: Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation. Med Phys 2007, 34:3054-3066.
- [12]Speight R, Sykes J, Lindsay R, Franks K, Thwaites D: The evaluation of a deformable image registration segmentation technique for semi-automating internal target volume (ITV) production from 4DCT images of lung stereotactic body radiotherapy (SBRT) patients. Radiother Oncol 2011, 98:277-283.
- [13]van Dam IE, de Koste JR v S, Hanna GG, Muirhead R, Slotman BJ, Senan S: Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. Radiother Oncol 2010, 96:67-72.
- [14]Zhang TZ, Orton NP, Tome WA: On the automated definition of mobile target volumes from 4D-CT images for stereotactic body radiotherapy. Med Phys 2005, 32:3493-3502.
- [15]Zhang T, Chi Y, Meldolesi E, Yan D: Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy. Int J Radiat Oncol Biol Phys 2007, 68:522-530.
- [16]Al-Mayah A, Moseley J, Hunter S, Velec M, Chau L, Breen S, Brock K: Biomechanical-based image registration for head and neck radiation treatment. Phys Med Biol 2010, 55:6491-6500.
- [17]Castadot P, Lee JA, Parraga A, Geets X, Macq B, GrÈgoire V: Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. Radiother Oncol 2008, 89:1-12.
- [18]Lu WG, Olivera GH, Chen Q, Ruchala KJ, Haimerl J, Meeks SL, Langen KM, Kupelian PA: Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy. Phys Med Biol 2006, 51:4357-4374.
- [19]van Elmpt W, Öllers M, van Herwijnen H, den Holder L, Vercoulen L, Wouters M, Lambin P, De Ruysscher D: Volume or position changes of primary lung tumor during (chemo-)radiotherapy cannot be used as a surrogate for mediastinal lymph node changes: the case for optimal mediastinal lymph node imaging during radiotherapy. Int J Radiat Oncol Biol Phys 2011, 79:89-95.
- [20]Vercauteren T, Pennec X, Perchant A, Ayache N: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 2009, 45(Suppl. 1):S61-S72.
- [21]Allaire S, Pekar V, Hope AJ, Breen SL, Jaffray DA: Automatic contour propagation in head & neck IGRT based on 3D salient interest points. Int J Radiat Oncol Biol Phys 2008, 72(Issue 1, Supplement):S87.
- [22]Wrangsjo A, Pettersson J, Knutsson H: Non-rigid registration using morphons. Image Anal Proc 2005, 3540:501-510.
- [23]Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxes D, Whitaker R: Engineering and algorithm design for an image processing API: a technical report on ITK - the insight toolkit. In Proc. of medicine meets virtual reality. Edited by Westwood J. Amsterdam: IOS Press; 2002.
- [24]Knutsson H, Morphons AM: Paint on priors and elastic canvas for segmentation and registration. in 14th Scandinavian conference, SCIA. Joensuu, Finland: Springer; 2005.
- [25]Janssens G, Jacques L, Orban de Xivry J, Geets X, Macq B: Diffeomorphic registration of images with variable contrast enhancement. Int J Biomed Imaging 2011, 2011:891585.
- [26]Black PE: Hausdorff distance. [Dictionary of algorithms and data structures [online] 2004 [cited 2011 april 2]] Available from: http://www.nist.gov/dads/HTML/hausdorffdst.html webcite
- [27]Steenbakkers RJ, Duppen JC, Fitton I, Deurloo KE, Zijp LJ, Comans EF, Uitterhoeve AL, Rodrigus PT, Kramer GW, Bussink J, De Jaeger K, Belderbos JS, Nowak PJ, van Herk M, Rasch CR: Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006, 64:435-48.
- [28]van Baardwijk A, Bosmans G, Boersma L, Buijsen J, Wanders S, Hochstenbag M, van Suylen RJ, Dekker A, Dehing-Oberije C, Houben R, Bentzen SM, van Kroonenburgh M, Lambin P, De Ruysscher D: PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007, 68:771-8.