ESTRO 2024 - Abstract Book

S4985

Physics - Radiomics, functional and biological imaging and outcome prediction

ESTRO 2024

[2] E. Subashi, E. LoCastro, A. Apte, M. J. Zelefsky, and N. Tyagi, “Quantitative Relaxometry for Target Localization and Response Assessment in Ultra-Hypofractionated MR-Guided Radiotherapy to the Prostate and DIL,” International Journal of Radiation Oncology*Biology*Physics, vol. 114, no. 3, p. S33, Nov. 2022, doi: 10.1016/j.ijrobp.2022.07.390. [3] A. Datta, M. C. Aznar, M. Dubec, G. J. M. Parker, and J. P. B. O’Connor, “Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions.,” Clin Oncol (R Coll Radiol), vol. 30, no. 11, pp. 702–710, Nov. 2018, doi: 10.1016/j.clon.2018.08.005. [4] E. S. Kooreman et al., “ADC measurements on the Unity MR-linac - A recommendation on behalf of the Elekta Unity MR-linac consortium.,” Radiother. Oncol., vol. 153, pp. 106–113, Dec. 2020, doi: 10.1016/j.radonc.2020.09.046. [5] D. Nguyen and O. Bieri, “Motion-insensitive rapid configuration relaxometry.,” Magn. Reson. Med., vol. 78, no. 2, pp. 518–526, Aug. 2017, doi: 10.1002/mrm.26384. [6] Y. Shcherbakova, C. A. T. van den Berg, C. T. W. Moonen, and L. W. Bartels, “PLANET: An ellipse fitting approach for simultaneous T1 and T2 mapping using phase-cycled balanced steady-state free precession.,” Magn. Reson. Med., vol. 79, no. 2, pp. 711–722, Feb. 2018, doi: 10.1002/mrm.26717. [7] Y. Zur, M. L. Wood, and L. J. Neuringer, “Motion-insensitive, steady-state free precession imaging.,” Magn. Reson. Med., vol. 16, no. 3, pp. 444–459, Dec. 1990, doi: 10.1002/mrm.1910160311. [8] M. J. van Rijssel, C. A. T. van den Berg, C. R. Noordman, and A. L. H. M. W. van Lier, “Accelerating phase-cycled bSSFP using sparsity across the phase-cycling dimension,” presented at the Joint Annual Meeting ISMRM-ESMRMB ISMRT 31st Annual Meeting, Concord, CA, Apr. 2022, doi: 10.58530/2022/4801. [9] J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement.,” Lancet, vol. 1, no. 8476, pp. 307–310, Feb. 1986, doi: 10.1016/S0140-6736(86)90837-8. [10] M. E. Bernardino, J. C. Chaloupka, J. A. Malko, J. L. Chezmar, and R. C. Nelson, “Are hepatic and muscle T2 values different at 0.5 and 1.5 Tesla?,” Magn. Reson. Imaging, vol. 7, no. 4, pp. 363–367, Aug. 1989. [11] T. H. Storås, K.-I. Gjesdal, Ø. B. Gadmar, J. T. Geitung, and N.-E. Kløw, “Prostate magnetic resonance imaging: multiexponential T2 decay in prostate tissue.,” J. Magn. Reson. Imaging, vol. 28, no. 5, pp. 1166–1172, Nov. 2008, doi: 10.1002/jmri.21534.

705

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A hierarchical Bayesian model to predict eGFR reduction following Kidney SBRT

Antony Carver 1 , Anjali Zarkar 2 , Helen Howard 1

1 University Hospitals Birmingham NHS Foundation Trust, Department of Medical Physics, Birmingham, United Kingdom. 2 University Hospitals Birmingham NHS Foundation Trust, Department of Oncology, Birmingham, United Kingdom

Purpose/Objective:

Recent studies have indicated that SBRT (Stereotactic Body Radiotherapy) for primary renal cell carcinoma is an effective treatment with local control rates at 2-3 years of approximately 95% or greater [1]. They have also found that eGFR (estimated Glomerular Filtration Rate) declines following treatment [2]. Patients can be monitored for dialysis or other intervention if their eGFR loss following treatment can be predicted from the demographic data, co morbidities and dosimetry data.