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Evaluation of statistical prediction model fitting by combining fiducial markers and lung volume for stereotactic body radiotherapy
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Y. Tanabe   , T. Deguchi , M. Kiritani , N. Hira , S. Tomimoto , H. Nishikawa , S. Tsumoto , H. Tanaka |
| Faculty of Medicine, Graduate School of Health Sciences, Okayama University, 2-5-1, Shikata, Kita, Okayama 700-8525, Japan , tanabey@okayama-u.ac.jp |
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Abstract: (494 Views) |
Background: This study evaluates the tracking accuracy of the lung tumor volume and fiducial markers using four-dimensional computed tomography (4DCT) and fitted neural network models. Materials and Methods: This study utilized data from 31 patients (109 fiducial markers) who underwent stereotactic body radiotherapy (SBRT). The respiratory movements of fiducial markers, lung tumors, and lung volume were calculated using 4DCT. Cross-correlation coefficients were then calculated to analyze the phase movements of fiducial markers, lung tumors, lung volume, direction displacement, and lung area (upper, middle, and lower lobes). Statistical prediction models were used to evaluate the predictive accuracy of the left–right (LR), anterior–posterior (AP), cranial–caudal (CC), and three-dimensional (3D) cross-correlation coefficients between fiducial markers and lung tumors. The coefficient of determination (R2) was used to determine the accuracy of the statistical prediction models for the explanatory variables. Results: The correlation between fiducial marker and lung tumor, as well as lung tumor movement by time phase, yielded the following R2 values—LR: 0.920, AP: 0.319, CC: 0.675, and 3D: 0.449 for the upper lobe; LR: 0.567, AP: 0.627, CC: 0.955, and 3D: 0.939 for the middle/lower lobes. Statistically significant differences were observed in the CC and 3D directions within each lower lobe. Conclusions: The respiratory movements of fiducial markers and lung tumors in lung SBRT show stronger correlation with the movement of lung volume in the middle/lower lobes compared to that in the upper lobes. Combining a fiducial marker with lung volume improves the prediction accuracy of the respiratory movement of lung tumors. |
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| Keywords: Pelvis, Stereotactic body radiotherapy, Four-dimensional computed tomography, Respiratory movement, Statistical prediction model, lung cancer. |
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Full-Text [PDF 602 kb]
(192 Downloads)
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Type of Study: Original Research |
Subject:
Radiation Biology
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| References |
1. Benedict SH, Yenice KM, Followill D, et al. (2010) Stereotactic body radiation therapy: The report of AAPM Task Group 101. Med Phys, 37(8): 4078-4101. [ DOI:10.1118/1.3438081] 2. Tan KV, Thomas R, Hardcastle N, et al. (2014) Predictors of respiratory-induced lung tumour motion measured on four-dimensional computed tomography. Clin Oncol, 27(4): 197-204. [ DOI:10.1016/j.clon.2014.12.001] 3. Shimizu S, Shirato H, Kagei K, et al. (2000) Impact of respiratory movement on the computed tomographic images of small lung tumors in three-dimensional (3D) radiotherapy. Int J Radiat Oncol Biol Phys, 46(5): 1127-1133. [ DOI:10.1016/S0360-3016(99)00352-1] 4. Wang Y, Liu T, Chen H, et al. (2020) Comparison of internal target volumes defined by three-dimensional, four-dimensional, and cone-beam computed tomography images of a motion phantom. Ann Transl Med, 8(22): 1488. [ DOI:10.21037/atm-20-6246] 5. Ge H, Cai J, Kelsey CR, Yin FF (2013) Quantification and minimization of uncertainties of internal target volume for stereotactic body radiation therapy of lung cancer. Int J Radiat Oncol Biol Phys, 85(2): 438-443. [ DOI:10.1016/j.ijrobp.2012.04.032] 6. Dahele M, Pearson S, Purdie T, et al. (2008) Practical considerations arising from the implementation of lung stereotactic body radiation therapy (SBRT) at a comprehensive cancer center. J Thorac Oncol, 3(11): 1332-1341. [ DOI:10.1097/JTO.0b013e31818b1771] 7. Goldstein JD, Lawrence YR, Appel S, et al. (2015) Continuous positive airway pressure for motion management in stereotactic body radiation therapy to the Lung: A controlled pilot study. Int J Radiat Oncol Biol Phys, 93(2015): 391-399. [ DOI:10.1016/j.ijrobp.2015.06.011] 8. Tanabe Y, Kiritani M, Deguchi T, et al. (2023) Patient-specific respiratory motion management using lung tumors vs fiducial markers for real-time tumor-tracking stereotactic body radiotherapy. Phys Imaging Radiat Oncol, 25: 100405. [ DOI:10.1016/j.phro.2022.12.002] 9. Ionascu D, Jiang SB, Nishioka S, et al. (2007) Internal-external correlation investigations of respiratory induced motion of lung tumors. Med Phys, 34(10): 3893-3903. [ DOI:10.1118/1.2779941] 10. Wong JW, Sharpe MB, Jaffray DA, et al. (1999) The use of active breathing control (ABC) to reduce margin for breathing motion. Int J Radiat Oncol Biol Phys, 44(4): 911-919. [ DOI:10.1016/S0360-3016(99)00056-5] 11. Garcia R, Oozeer R, Le Thanh HL, et al. (2002) Radiotherapy of lung cancer: the inspiration breath hold with a spirometric monitoring. Cancer Radiother, 6(1): 30-38. [ DOI:10.1016/S1278-3218(01)00132-9] 12. Tanabe Y and Tanaka H (2022) Statistical evaluation of the effectiveness of dual amplitude-gated stereotactic body radiotherapy using fiducial markers and lung volume. Phys Imaging Radiat Oncol, 24: 82-87. [ DOI:10.1016/j.phro.2022.10.001] 13. Tong Z, Liu Y, Ma H, et al. (2020) Development, validation and comparison of artificial neural network models and logistic regression models predicting survival of unresectable pancreatic cancer. Front Bioeng Biotechnol, 13(8): 196. [ DOI:10.3389/fbioe.2020.00196] 14. Lehmann LJ, Tim C, Cofala M, et al. (2021) Machine learning in oncology-Perspectives in patient-reported outcome research. Der Onkologe, 27: 150-155. [ DOI:10.1007/s00761-021-00916-9] 15. Liu J, Sun L, Lu X, et al. (2023) Development and validation of a stacking nomogram for predicting regional lymph node metastasis status in rectal cancer via deep learning and hand-crafted radiomics. Int J Radiat Res, 21(2): 267-274. 16. Tanabe Y and Eto H (2022) Evaluation of patient-specific motion management for radiotherapy planning computed tomography using a statistical method. Med Dosim, 47(2): e13-e18. [ DOI:10.1016/j.meddos.2021.12.002] 17. Schaff DP and Waldhauser F (2005) Movement cross-correlation-based differential travel-time measurements at the Northern California Seismic Network. Bull Seismol Soc Am, 95(6): 2446-2461. [ DOI:10.1785/0120040221] 18. Gotwalt CM (2012) JMP neural network methodology. SAS Institute, Cary. 1-11. 19. Feenstra DR, Molotnikov A, Birbilis N (2021) Utilization of artificial neural networks to rationalize processing windows in directed energy deposition applications. Mater Des. 198(15): 109342. [ DOI:10.1016/j.matdes.2020.109342] 20. Jonas W, Baho S, Chunyu W, et al. (2022) Four-dimensional computed tomography-based correlation of respiratory motion of lung tumors with implanted fiducials and an external surrogate. Advances in Radiation Oncology, 7(3): 100855. [ DOI:10.1016/j.adro.2021.100885] 21. Keall PJ, Mageras GS, Balter JM, et al. (2006) The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys, 33(10): 3874-3900. [ DOI:10.1118/1.2349696] |
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Tanabe Y, Deguchi T, Kiritani M, Hira N, Tomimoto S, Nishikawa H, et al . Evaluation of statistical prediction model fitting by combining fiducial markers and lung volume for stereotactic body radiotherapy. Int J Radiat Res 2024; 22 (1) :17-22 URL: http://ijrr.com/article-1-5207-en.html
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