Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study

Technology in Cancer Research and Treatment - Tập 9 Số 6 - Trang 551-561 - 2010
Ahmad Esmaili Torshabi1, Andrea Pella2, Marco Riboldi2, G. Baroni1,2
1Biongineering Unit, Centro Nazionale di Adroterapia Oncologica, Strada privata Campeggi s.n.c., 27100 Pavia, IT
2TBMLab-Department of Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133 Milano, IT

Tóm tắt

The use of external surrogates to predict tumor motion in real-time for extra-cranial sites requires the use of accurate correlation models. This is extremely challenging when motion prediction is to be performed over several breathing cycles, as occurs for realtime tumor tracking with Cyberknife® Synchrony®. In this work we compare three different approaches to infer tumor motion based on external surrogates, since no comparative study is available to assess the accuracy of correlation models in tumor tracking over a long time period. We selected 20 cases in a database of 130 patients treated with realtime tumor tracking by means of the Synchrony® module. The implemented correlation models comprise linear/quadratic correlation, artificial neural networks and fuzzy logic. The accuracy of each correlation model is evaluated on the basis of ground truth tumor position information acquired during treatment, as detected by means of stereoscopic X-ray imaging. Results show that the implemented models achieve an error reduction with respect to Synchrony®, measured at the 95% confidence level, up to 10.8% for the fuzzy logic approach. This latter is able to partly reduce the incidence of tumor tracking errors above 6 mm, resulting in improved accuracy for larger discrepancies. In conclusion, complex models are suggested to predict tumor motion over long time periods. This leads to an effective improvement with respect to Cyberknife® Synchrony®. Future studies will investigate the sensitivity of the implemented models to the input database, in order to define optimal strategies.

Từ khóa


Tài liệu tham khảo

10.1016/S0360-3016(99)00154-6

10.1016/S0360-3016(00)00747-1

10.1016/S0360-3016(00)00524-1

10.1088/0031-9155/41/1/007

10.1016/0360-3016(89)90078-3

10.1016/S0360-3016(00)00748-3

10.1016/S0140-6736(99)00700-X

10.1016/S0360-3016(02)02803-1

10.1088/0031-9155/51/22/012

10.1118/1.2739811

10.3816/CLC.2007.n.033

Hoogeman M., 2009, Radiation Oncology, 74, 297

10.1118/1.2349696

10.1088/0031-9155/50/19/020

10.1088/0031-9155/54/19/005

10.1118/1.1771931

Ramrath L., 2007, Proceedings of the 21st International Conference and Exhibition on Computer Assisted Radiology and Surgery (CARs'07), 21

10.1088/0031-9155/53/11/011

10.1088/0031-9155/49/3/006

10.1016/S1388-2457(02)00033-0

10.1118/1.1835611

10.1109/91.755393

10.1016/S0165-0114(97)00106-1

10.1109/91.824766

10.1109/2.53

10.1109/69.43406

Rosenblatt F., 1958, The perceptron: a probabilistic model for information storage and organization in the brain Psychological review, 65, 386

10.1007/BF00332914

10.1090/qam/10666

10.1137/0111030

10.1145/331499.331504

10.1080/01969727308546046

10.1007/978-1-4757-0450-1

10.1109/TSMCB.2003.810951

10.1109/3477.678624

10.1109/TSMC.1985.6313399

Procházka A., 2007, In Proceedings the 5th IEEE Int. Conference on Computational Cybernetics

10.1016/S0140-6736(96)90609-1

10.1016/S0933-3657(03)00065-4

Thông tin
Thông tin xuất bản

Technology in Cancer Research and Treatment

Tập 9 Số 6

551-561

Thông tin tác giả