EFFECT OF PATIENT’S GENDER ON THE CALCULATION OF TIME-INTEGRATED ACTIVITY COEFFICIENT IN RADIONUCLIDE THERAPY: STUDY WITH NON-LINEAR MIXED-EFFECTS MODEL

Penulis

  • Assyifa Rahman Hakim Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
  • Fira Dwi Ananda Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
  • Indra Budiansah Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
  • Rien Ritawidya Research Center for Radioisotope, Radiopharmaceutical, and Biodosimetry Technology, National Research and Innovation Agency (BRIN), Tangerang Selatan 15314, Indonesia.
  • Deni Hardiansyah Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.

DOI:

https://doi.org/10.21009/03.1401.FA03

Abstrak

Purpose: This study investigates the influence of patient gender on the calculation of the Time-Integrated Activity Coefficient (TIAC) in radionuclide therapy. Methods: Kidney biokinetic data (PMID: 33443063) from 10 patients (6 males and 4 females) treated with [¹⁷⁷Lu]Lu-DOTATATE were analysed. A bi-exponential function was used to model both uptake and clearance phases. The reference TIAC (rTIAC) was obtained by fitting the bi-exponential parameters to the complete dataset using a Non-Linear Mixed-Effects Model (NLMEM). To assess the impact of gender, separate NLMEM fits were performed for male and female subgroups to generate estimated TIACs (eTIACs). rTIAC and eTIAC values were compared using the Relative Deviation (RD) and the Root-Mean-Square Error (RMSE). Gender was considered influential if RD or RMSE exceeded 27%. Results: For male patients, the RD was −4.1% ± 7.2% and the RMSE was 8.3%. For female patients, the RD was 5.2% ± 7.8% and the RMSE was 9.4%. Conclusions: Based on the analysed [¹⁷⁷Lu]Lu-DOTATATE biokinetic data, gender does not appear to be a major determinant in TIAC calculation. The deviations between rTIAC and eTIAC remain within an acceptable range for both male and female patients.

Referensi

[1] Bray, F., et al. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229–263. https://doi.org/10.3322/caac.21834

[2] van der Wal, B. C. H., & Dadachova, E. (2023). Targeted radionuclide therapy of cancer and infections. International Journal of Molecular Sciences, 24(10), 9081. https://doi.org/10.3390/ijms24109081

[3] Giraudet, A. L. (2019). Radionuclide therapy targeting PSMA for the treatment of metastatic prostate cancer: Current point of view and ways of improvement. Médecine Nucléaire, 43(3), 275–279. https://doi.org/10.1016/j.mednuc.2019.03.003

[4] Sjögreen Gleisner, K., et al. (2022). EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands. European Journal of Nuclear Medicine and Molecular Imaging, 49(6), 1778–1809. https://doi.org/10.1007/s00259-022-05727-7

[5] Ivashchenko, O. V., et al. (2024). Time-Activity data fitting in molecular radiotherapy: Methodology and pitfalls. Physica Medica, 117, 103192. https://doi.org/10.1016/j.ejmp.2023.103192

[6] Zvereva, A., Kamp, F., Schlattl, H., Zankl, M., & Parodi, K. (2018). Impact of interpatient variability on organ dose estimates according to MIRD schema: Uncertainty and variance-based sensitivity analysis. Medical Physics, 45(7), 3391–3403. https://doi.org/10.1002/mp.12984

[7] Philipp, M., Buatois, S., Retout, S., & Mentré, F. (2024). Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: Comparison of the full model, SCM and SCM+ approaches. Journal of Pharmacokinetics and Pharmacodynamics, 51(6), 653–670. https://doi.org/10.1007/s10928-024-09911-0

[8] Sanghavi, K., et al. (2024). Covariate modeling in pharmacometrics: General points for consideration. CPT: Pharmacometrics & Systems Pharmacology, 13(5), 710–728. https://doi.org/10.1002/psp4.13115

[9] Al-Jabri, A., Cooke, J., Cournane, S., & Healy, M.-L. (2021). Gender differences in estimating I-131 thyroid uptake from Tc-99m thyroid uptake for benign thyroid disease. British Journal of Radiology, 94(1118). https://doi.org/10.1259/bjr.20200700

[10] Lin, W. Y., & Wang, S. J. (1998). Influence of age and gender on quantitative sacroiliac joint scintigraphy. Journal of Nuclear Medicine, 39(7), 1269–1272.

[11] Mazinani, M., Tajik-Mansoury, M. A., Sabour, M., Jadidi, M., Peer-Firozjaei, M., & Asadian, N. (2022). Assessment relation of myocardial detector counts and administered activity of 99mTc-SestaMIBI in MPI: The effects of body weight, BMI, and gender. Current Radiopharmaceuticals, 15(2), 117–122. https://doi.org/10.2174/1874471014666210426112933

[12] Wang, C., Peterson, A. B., Wong, K. K., Roseland, M. E., Schipper, M. J., & Dewaraja, Y. K. (2023). Single-time-point imaging for dosimetry after [177Lu]Lu-DOTATATE: Accuracy of existing methods and novel data-driven models for reducing sensitivity to time-point selection. Journal of Nuclear Medicine, 64(9), 1463–1470. https://doi.org/10.2967/jnumed.122.265338

[13] Devasia, T. P., Dewaraja, Y. K., Frey, K. A., Wong, K. K., & Schipper, M. J. (2021). A novel time–activity information-sharing approach using nonlinear mixed models for patient-specific dosimetry with reduced imaging time points: Application in SPECT/CT after 177Lu-DOTATATE. Journal of Nuclear Medicine, 62(8), 1118–1125. https://doi.org/10.2967/jnumed.120.256255

[14] Hardiansyah, D., Riana, A., Eiber, M., Beer, A. J., & Glatting, G. (2023). Population-based model selection for an accurate estimation of time-integrated activity using non-linear mixed-effects modelling. Zeitschrift für Medizinische Physik. https://doi.org/10.1016/j.zemedi.2023.01.007

[15] Hardiansyah, D., et al. (2021). A population-based method to determine the time-integrated activity in molecular radiotherapy. EJNMMI Physics, 8(1). https://doi.org/10.1186/s40658-021-00427-x

[16] Hardiansyah, D., Riana, A., Beer, A. J., & Glatting, G. (2023). Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: A proof of concept. EJNMMI Physics, 10(1). https://doi.org/10.1186/s40658-023-00530-1

[17] Bonate, P. L. (2011). Pharmacokinetic–Pharmacodynamic Modeling and Simulation. Springer. https://doi.org/10.1007/978-1-4419-9485-1

[18] Bach, T., & An, G. (2021). Comparing FOCE and different EM methods in NONMEM: Real data experience with a complex nonlinear parent-metabolite PK model. Journal of Pharmacokinetics and Pharmacodynamics, 48(4), 581–595. https://doi.org/10.1007/s10928-021-09753-0

[19] Gomes, C. V., et al. (2025). Characterization of effective half-life for instant single-time-point dosimetry using machine learning. Journal of Nuclear Medicine, 66(5), 778–784. https://doi.org/10.2967/jnumed.124.268175

[20] Svensson, R. J., & Jonsson, E. N. (2022). Efficient and relevant stepwise covariate model building for pharmacometrics. CPT: Pharmacometrics & Systems Pharmacology, 11(9), 1210–1222. https://doi.org/10.1002/psp4.12838

[21] Zou, Y., Tang, F., & Ng, C. M. (2021). A modified hybrid Wald’s approximation method for efficient covariate selection in population pharmacokinetic analysis. AAPS Journal, 23(2), 37. https://doi.org/10.1208/s12248-021-00572-2

Diterbitkan

2025-12-07

Cara Mengutip

Assyifa Rahman Hakim, Fira Dwi Ananda, Indra Budiansah, Rien Ritawidya, & Deni Hardiansyah. (2025). EFFECT OF PATIENT’S GENDER ON THE CALCULATION OF TIME-INTEGRATED ACTIVITY COEFFICIENT IN RADIONUCLIDE THERAPY: STUDY WITH NON-LINEAR MIXED-EFFECTS MODEL. Joint Prosiding IPS Dan Seminar Nasional Fisika, 14(1), FA 17–23. https://doi.org/10.21009/03.1401.FA03