Initial clinical experience with Epid-based in-vivo dosimetry for VMAT treatments of head-and-neck tumors

2016 ◽  
Vol 32 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Savino Cilla ◽  
Daniela Meluccio ◽  
Andrea Fidanzio ◽  
Luigi Azario ◽  
Anna Ianiro ◽  
...  
Head & Neck ◽  
2006 ◽  
Vol 28 (8) ◽  
pp. 750-760 ◽  
Author(s):  
Barbara Alicja Jereczek-Fossa ◽  
Marco Krengli ◽  
Roberto Orecchia

2020 ◽  
Vol 21 (11) ◽  
pp. 3973
Author(s):  
Mario P. Carante ◽  
Giulia Aricò ◽  
Alfredo Ferrari ◽  
Christian P. Karger ◽  
Wioletta Kozlowska ◽  
...  

(1) Background: Cancer ion therapy is constantly growing thanks to its increased precision and, for heavy ions, its increased biological effectiveness (RBE) with respect to conventional photon therapy. The complex dependence of RBE on many factors demands biophysical modeling. Up to now, only the Local Effect Model (LEM), the Microdosimetric Kinetic Model (MKM), and the “mixed-beam” model are used in clinics. (2) Methods: In this work, the BIANCA biophysical model, after extensive benchmarking in vitro, was applied to develop a database predicting cell survival for different ions, energies, and doses. Following interface with the FLUKA Monte Carlo transport code, for the first time, BIANCA was benchmarked against in vivo data obtained by C-ion or proton irradiation of the rat spinal cord. The latter is a well-established model for CNS (central nervous system) late effects, which, in turn, are the main dose-limiting factors for head-and-neck tumors. Furthermore, these data have been considered to validate the LEM version applied in clinics. (3) Results: Although further benchmarking is desirable, the agreement between simulations and data suggests that BIANCA can predict RBE for C-ion or proton treatment of head-and-neck tumors. In particular, the agreement with proton data may be relevant if the current assumption of a constant proton RBE of 1.1 is revised. (4) Conclusions: This work provides the basis for future benchmarking against patient data, as well as the development of other databases for specific tumor types and/or normal tissues.


2012 ◽  
Vol 05 (04) ◽  
pp. 1250028 ◽  
Author(s):  
YING ZHENG ◽  
QIAOYA LIN ◽  
HONGLIN JIN ◽  
JUAN CHEN ◽  
ZHIHONG ZHANG

The development of experimental animal models for head and neck tumors generally rely on the bioluminescence imaging to achieve the dynamic monitoring of the tumor growth and metastasis due to the complicated anatomical structures. Since the bioluminescence imaging is largely affected by the intracellular luciferase expression level and external D-luciferin concentrations, its imaging accuracy requires further confirmation. Here, a new triple fusion reporter gene, which consists of a herpes simplex virus type 1 thymidine kinase (TK) gene for radioactive imaging, a far-red fluorescent protein (mLumin) gene for fluorescent imaging, and a firefly luciferase gene for bioluminescence imaging, was introduced for in vivo observation of the head and neck tumors through multi-modality imaging. Results show that fluorescence and bioluminescence signals from mLumin and luciferase, respectively, were clearly observed in tumor cells, and TK could activate suicide pathway of the cells in the presence of nucleotide analog-ganciclovir (GCV), demonstrating the effectiveness of individual functions of each gene. Moreover, subcutaneous and metastasis animal models for head and neck tumors using the fusion reporter gene-expressing cell lines were established, allowing multi-modality imaging in vivo. Together, the established tumor models of head and neck cancer based on the newly developed triple fusion reporter gene are ideal for monitoring tumor growth, assessing the drug therapeutic efficacy and verifying the effectiveness of new treatments.


2015 ◽  
Vol 115 ◽  
pp. S852
Author(s):  
S. Cilla ◽  
D. Meluccio ◽  
A. Fidanzio ◽  
L. Azario ◽  
F. Greco ◽  
...  

2017 ◽  
Vol 42 ◽  
pp. 157-161 ◽  
Author(s):  
R. Consorti ◽  
A. Fidanzio ◽  
V. Brainovich ◽  
F. Mangiacotti ◽  
M. De Spirito ◽  
...  

2008 ◽  
Vol 7 (1) ◽  
pp. 7290.2008.0006 ◽  
Author(s):  
Liang Shan ◽  
Yubin Hao ◽  
Songping Wang ◽  
Alexandru Korotcov ◽  
Renshu Zhang ◽  
...  

2016 ◽  
Vol 32 ◽  
pp. 14
Author(s):  
S. Cilla ◽  
A. Ianiro ◽  
F. Deodato ◽  
G. Macchia ◽  
C. Digesu ◽  
...  

2008 ◽  
Vol 101 (7) ◽  
pp. 541-547 ◽  
Author(s):  
Kanako Noda ◽  
Takashi Hirano ◽  
Akira Matsumoto ◽  
Masashi Suzuki

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