Determination of Optimal Normal Tissue Objective Settings for Radiation Therapy Planning of Brain Tumor

liza indrayani, Choirul Anam, Heri Sutanto, Rinarto Subroto

Abstract


Normal Tissue Objective (NTO) is a tool used in inverse-planned of intensity-modulated radiation therapy (IMRT) to limit dose spreading to normal tissues. Only a few studies in the literatures has determined the optimal NTO for treatment plans. There is no information regarding the optimal NTO setting for the brain tumor cases. Therefore, the aim of this study was to determine the NTO based on the dose distribution of brain tumor radiation therapy. 15 patients were re-planned using NTO priority of 100 for automatic and manual NTO. Manual NTO were re-planned using a fix start dose f_0= 105% and end dose f_∞=60%, where k varied from fall-off 0.1 〖mm〗^(-1) to a much steeper fall-off 1 〖mm〗^(-1) and margin to planning target volume (PTV) x_start varied from 0 to 10 mm. Planning was evaluated using several indices: conformity index (CI), homogeneity index (HI), gradient index (GI), and modified gradient index (mGI). Differences between automatic and manual NTO were evaluated using the Wilcoxon signed rank test. In this study, we obtained the manual NTO with x_start=1 mm and dose fall off 〖≥1 mm〗^(-1) is the most optimal result. Comparisons results of automatic and manual NTOs were: CI of 0.92 vs 0.98 (p= 0.001), HI of 1.09 vs 1.11 (p = 0.004), GI of 5.00 vs 4.73 (p = 0.002), mGI of 4.46 vs 3.77 (p = 0.001). Based on these indices, manual NTO shows a better treatment plan than automatic NTO. It is proved by the dose reduction in OAR after applied manual NTO on planning.
Keywords: Normal Tissue Objective (NTO), priority, dose fall off, external beam radiation therapy, IMRT

Keywords


Normal Tissue Objective (NTO), priority, dose fall off, external beam radiation therapy, IMRT

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Mazzola R., Fiorentino A., Ricchetti,F. Gregucci F., Corradini S., and Alongi F., 2018. “An update on radiation therapy in head and neck cancers,”. Expert Rev. Anticancer Ther., vol. 18, no. 4, pp. 359–364.

Xu J., Xue., Mengzhi L., Jun H, Zheng C., jinyu Y., yan D., Chengtao S., 2018, “Somatic mitochondrial DNA D ‑ loop mutations in meningioma discovered : A preliminary data A comprehensive overview of mitochondrial DNA 4977-bp,” J. Cancer Res. Ther., vol. 14, no. 7, pp. 1525–1534.

Dunlop, A., Welsh, L., McQuaid, D., Dean, J., Gulliford, S., Hansen, V., Bhide, S., Nutting, C., Harrington, K., Newbold, K., 2015; ‘Brain-sparing methods for IMRT of head and neck cancer’. PloS ONE. vol. 10, no 3, pp. 1–13. doi: 10.1371/journal.pone.0120141.

Vargo J.A., Brian A., Dimitris N., Mihailidis., Jack Mallah., Matthew Plants., Christine A. Welch., Grant M. Clark., loyd J. Farinash., Prem Raja., Michael B. Harmon., Lewis A. Whaley., 2011,“Early clinical outcomes for 3 radiation techniques for brain metastases: Focal versus whole-brain,” Pract. Radiat. Oncol., vol. 1, no. 4, pp. 261–270.

Zayat D. M., Attalla E. M., Abouelenein H. S., Elkem Y. M., and Khalil W., 2014, “Dosimetric Comparison of Intensity-Modulated Radiotherapy versus Three-Dimensional Conformal Radiotherapy for Patients with Brain Tumors,” Open J. Radiol., vol. 04, no. 01, pp. 85–96.

Xhaferllari, I., Wong, E., Bzdusek, K., Lock, M., Chen, J. Z. 2013. ‘Automated IMRT planning with regional optimization using planning scripts’. Journal of Applied Clinical Medical Physics. vol. 14, no 1, pp. 176–191. doi: 10.1120/jacmp.v14i1.4052.

Fogliata, A., Reggiori, G., Stravato, A., Lobefalo, F., Franzese, C., Franceschini, D., Tomatis, S., Mancosu, P., Scorsetti, M., Cozzi, L. 2017. ‘RapidPlan head and neck model: The objectives and possible clinical benefit’. Radiation Oncology. Radiation Oncology, vol 12, no 1, pp. 1–12. doi: 10.1186/s13014-017-0808-x.

Corkum, M. T., Mitchell, S., Venkatesan, V., Read, N., Warner, A., Palma, D. A. 2019. ‘Does 5 + 5 Equal Better Radiation Treatment Plans in Head and Neck Cancers?’. Advances in Radiation Oncology. The Author(s), vol. 4, no 4, pp. 683–688. doi: 10.1016/j.adro.2019.06.001.

Wang, D., Denittis, A., dan Hu, Y. 2020. ‘Strategies to optimize stereotactic radiosurgery plans for brain tumors with volumetric ‐ modulated arc therapy’. Journal of Applied Clinical Medical Physics. vol 21, no 3, pp. 45–51. doi: 10.1002/acm2.12818. Epub 2020 Feb 11. PMID: 32043810; PMCID: PMC7075387

Caldeira A., trinca W.A., flores T.P., mariano F., Brito C.S., Gr€ussner M. M., Costa B., 2020, “The Influence of Normal Tissue Objective in the Treatment of Prostate Cancer,” J. Med. Imaging Radiat. Sci., vol. 51, no. 2, pp. 312–316.

Varian medical system. 2014. 'Eclipse Photon and Electron Algorithms Reference Guide 13485'. 2001-2015 Varian Medical Systems, Inc.

Fogliata, A., Thompson, S., Stravato, A., Tomtis, S., Scorsetti, M., Cozzi, L. 2018.‘On the gEUD biological optimization objective for organs at risk in Photon Optimizer of Eclipse treatment planning system’, Journal of Applied Clinical Medical Physics, vol 19, no 1, pp. 106–114. doi: 10.1002/acm2.12224.

Bell, J. P., Patel P., Higgins K., McDonald M. W., dan Roper J. 2018. ‘Fine-tuning the normal tissue objective in eclipse for lung stereotactic body radiation therapy’. Medical Dosimetry. Elsevier Inc., vol 43, no 4, pp. 344– 350. doi: 10.1016/j.meddos.2017.11.004.

Marks L.B., yorke E.D., jackson A., haken E. K., constine L.S., eisbruch A., bentzen S. M., nam J., deasy J. O., 2010, et al., “Use of Normal Tissue Complication Probability Models in the Clinic,” Int. J. Radiat. Oncol. Biol. Phys., vol. 76, no. 3 SUPPL.

Lomax, N. J. dan Scheib, S. G. 2003. ‘Quantifying the degree of conformity in radiosurgery treatment planning’. International Journal of Radiation Oncology Biology Physics. vol 55, no 5, pp. 1409–1419. doi: 10.1016/S0360-3016(02)04599-6.

Shaw E.M.D., Scott C., Soouhami L., Dinapoli R., Kline R., Loeddler J., Farnan N. 2000. 'single dose radiosurgical treatment of recurrent previously irradiated primary brain tumors and brain metastases : final report of rtog protocol 90-05'. vol. 47, no. 2, pp. 291–298, 2000.

Paddick I., Lippitz B. 2006. 'A simple dose gradient measurement tool to complement the conformity index'. journal Neurosurgery. vol. 105, pp. 194–201.

Ohtakara, K., Hayashi, S. dan Hoshi, H. 2011.‘Dose gradient analyses in linac- based intracranial stereotactic radiosurgery using paddick’s gradient index: Considerationof the optimal method for plan evaluation’. Journal of Radiation Research, vol 52, no 5, pp. 592–599. doi: 10.1269/jrr.11005.

ICRU report 62. 1999. "(ICRU report 62) prescribing, recording, and reporting photon beam therapy"., International commision on radiation units and measurements (ICRU).

Cao, T., Dai Z., Ding, Z., Li W., Quan, H. 2019. ‘Analysis of different evaluation indexes for prostate stereotactic body radiation therapy plans: conformity index, homogeneity index and gradient index’. Precision Radiation Oncology. vol 3, no 3, pp. 72–79. doi: 10.1002/pro6.1072.

Aiyama H., yamamoto M., kawabe T., watanabe S., koiso T., sato Y., higuchi Y., ishika E., 2018. “Clinical significance of conformity index and gradient index in patients undergoing stereotactic radiosurgery for a single metastatic tumor,” J Neurosurg (Suppl) vol. 129, no. December, pp. 103–110.

Ohtakara, K., Hayashi, S. dan Hoshi, H. 2011.‘Dose gradient analyses in linac- based intracranial stereotactic radiosurgery using paddick’s gradient index: Considerationof the optimal method for plan evaluation’. Journal of Radiation Research, vol 52, no 5, pp. 592–599. doi: 10.1269/jrr.11005.

Paddick I., Lippitz B. 2006. 'A simple dose gradient measurement tool to complement the conformity index'. journal Neurosurgery. vol. 105, pp. 194–201.




DOI: http://dx.doi.org/10.52155/ijpsat.v31.2.4103

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