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All Journal Journal of Medical Physics and Biophysics Jurnal Fisika FLUX PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) BULETIN FISIKA
Deni Hardiansyah
Universitas Indonesia
Astronomy Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics Other

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Review singkat dosimetri internal di kedokteran nuklir menggunakan Physiologically-based Pharmacokinetic (PBPK) model dan topik penelitian paper JMPB volume 7, No. 1 (2020) Deni Hardiansyah
Journal of Medical Physics and Biophysics Vol 7, No 1 (2020)
Publisher : Indonesian Association of Physicists in Medicine (AIPM/AFISMI)

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Abstract

Seleksi Model pada Peptide-receptor Radionuclide Therapy dengan 177Lu-DOTATATE untuk Dosimetri Ginjal Rahmatul Hanifah; Nur Atikah; M Zaidi Anwari; M Dlorifun Naqiyyun; Ade Riana; Deni Hardiansyah
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 19, No 2 (2022): Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat
Publisher : Lambung Mangkurat University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.548 KB) | DOI: 10.20527/flux.v19i2.12153

Abstract

Seleksi model merupakan aspek penting dari analisis data farmakokinetik. Seleksi model dilakukan untuk memperoleh fungsi terbaik yang selanjutnya digunakan dalam perhitungan nilai TIACs pada dosimetri individu. Data yang digunakan pada penelitian ini berupa data biodistribusi radiofarmaka 177Lu-DOTATATE pada organ ginjal dari 8 pasien PRRT. Setiap data pasien difitting dengan menggunakan beberapa fungsi. Pada setiap fungsi yang diterapkan, dilakukan analisa goodness of fit. Pada setiap fungsi yang memenuhi kriteria goodness of fit dilakukan perhitungan nilai AICc dan nilai pembobotan AICc. Fungsi dengan nilai pembobotan AICc terbesar dipilih menjadi fungsi terbaik. Berdasarkan proses seleksi model yang dilakukan, fungsi f2a,1ex  diperoleh sebagai fungsi terbaik untuk pasien 1,3,4,5,6, dan 7. Sementara itu, fungsi f2b,1ex  menjadi model terbaik untuk pasien 2 dan fungsi f1a,1ex untuk pasien 8.
EVALUATING RENAL TIME-INTEGRATED ACTIVITY COEFFICIENT IN [177Lu]Lu-DOTA-TATE THERAPY: SIMULTANEOUS VS. SEPARATE KIDNEY MODELING USING NON-LINEAR MIXED-EFFECTS MODELING Indra Budiansah; Assyifa Rahman Hakim; Fira Dwi Ananda; Supriyanto Ardjo Pawiro; Deni Hardiansyah
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA01

Abstract

This study aimed to compare renal time-integrated activity coefficients (TIACs) in [177Lu]Lu-DOTA- TATE therapy using non-linear mixed-effects modeling (NLMEM), by evaluating the effect of the fitting setting method of left and right kidney biokinetic data to TIAC calculation. Renal biokinetic data of [177Lu]Lu-DOTA-TATE were collected from ten patients with neuroendocrine tumors from literature (PMID:33443063). SPECT/CT imaging was performed between days 1 and 7 after-injection. The bi-exponential function parameters were fitted to biokinetic data using NLMEM performed using NONMEM software, with two fitting approaches: simultaneous fitting of both kidneys and separate fitting of the left and right kidneys. TIACs from the simultaneous fitting were defined as simultaneous TIACs (siTIACs), and those from separate fitting as separated TIACs (seTIACs). The differences between siTIACs and seTIACs were assessed using relative deviations (RDs), with seTIACs considered equivalent to siTIACs if RD was below 5%. The bi-exponential function successfully describes the renal biokinetic data. seTIACs showed good agreement with the siTIACs, with median[min, max] RD of -1.6[-4.5, 0.8]%. Simultaneous fitting of left and right kidneys biokinetic data using the NLMEM approaches produced similar TIACs to those obtained from separate fittings. Therefore, TIACs from simultaneous and separate fittings of renal biokinetic data are comparable and clinically applicable.
THE EFFECTS OF INTRA-INDIVIDUAL VARIABILITY SETTING ON THE ACCURACY OF TIME-INTEGRATED ACTIVITY CALCULATIONS USING NONLINEAR MIXED-EFFECTS MODELING Fira Dwi Ananda; Assyifa Rahman Hakim; Indra Budiansah; Rien Ritawidya; Deni Hardiansyah
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA04

Abstract

Estimasi yang akurat terhadap time-integrated activity (TIA) penting untuk perencanaan pengobatan pada peptide receptor radionuclide therapy (PRRT). Dalam konteks pemodelan farmakokinetik, intra-individual variability (IAV) — yang berkaitan dengan ketidakpastian pengukuran — dapat memengaruhi estimasi nilai TIA. Penelitian ini bertujuan untuk meneliti dampak variasi pengaturan IAV terhadap perhitungan TIA. Data biokinetik ginjal (PMID: 33443063) dari 10 pasien tumor neuroendokrin setelah pemberian [¹⁷⁷Lu]Lu-DOTATATE dianalisis menggunakan SPECT/CT. Estimasi TIA dilakukan menggunakan Nonlinear Mixed-Effects Modeling (NLMEM) dengan error model proporsional. Metode 1: TIA referensi (rTIA) dihitung dengan mengestimasi inter-individual variability (IIV) and IAV. Metode 2: Nilai IAV kemudian ditetapkan menjadi setengah (hTIA) dan dua kali lipat (tTIA) dari nilai yang diperoleh pada Metode 1. Pengaruh perubahan IAV terhadap akurasi TIA dievaluasi dengan membandingkan hTIA dan tTIA terhadap rTIA menggunakan relative deviation (RD), root-mean-square error (RMSE), dan mean absolute percentage error (MAPE). Hasil menunjukkan bahwa penetapan IAV menjadi setengah dari nilai referensi menghasilkan RMSE sebesar 5% dan MAPE sebesar 3%. Sementara itu, perubahan dua kali nilai IAV menghasilkan RMSE sebesar 7% dan MAPE sebesar 6%. Secara keseluruhan, perubahan pengaturan IAV memberikan dampak yang sangat kecil, karena perhitungan TIA tetap stabil dan tidak sensitif terhadap variasi IAV, baik ketika nilainya dikurangi setengah maupun digandakan dalam data biokinetik populasi ini.
EFFECT OF PATIENT’S GENDER ON THE CALCULATION OF TIME-INTEGRATED ACTIVITY COEFFICIENT IN RADIONUCLIDE THERAPY: STUDY WITH NON-LINEAR MIXED-EFFECTS MODEL Assyifa Rahman Hakim; Fira Dwi Ananda; Indra Budiansah; Rien Ritawidya; Deni Hardiansyah
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA03

Abstract

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.
Impact of Training Dataset Size on the Accuracy of L-SVR Single-Time-Point Renal Dosimetry for [¹⁷⁷Lu]Lu-PSMA-617 Therapy Abdurrahman Aziz Wicaksono; Jaja Muhammad Jabar; Syahril Siregar; Deni Hardiansyah
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p07

Abstract

Radiopharmaceutical therapy (RPT) using [¹⁷⁷Lu]Lu-PSMA-617 requires accurate dosimetry to evaluate organs-at-risk (OAR), specifically the kidneys. Single-time-point (STP) dosimetry simplifies clinical workflows by reducing SPECT/CT acquisition. Machine learning (ML) offers a potential solution, yet clinical implementation is hindered by the scarcity of sufficient training datasets for ML-based studies. This study investigated the relationship between training dataset size and time-integrated activity (TIA) estimation accuracy. A Linear Support Vector Regression (L-SVR) model was trained on synthetic virtual patients (VPs, 5,000 total) simulated from a published PBMS NLMEM renal biokinetics at five imaging times (t=1.8 h, 18.7 h, 42.6 h, 66.2 h, and 160.3 h). Time-activity-curve (TAC) and reference TIA (rTIA) were calculated for each VP. Random sampling was performed in increasing dataset sizes. Sample sizes were sub-sampled to training (80%) and testing (20%) datasets. L-SVR was trained on STP data at 42.6 h post-injection (best-time-point of PBMS NLMEM study) from the training dataset and tested by generating estimated TIA (eTIA) with input from the testing dataset. Performance was evaluated by calculating root-mean-square-error (RMSE) and mean-absolute-percentage-error (MAPE) of the eTIA to rTIA. Results showed that the accuracy of eTIA from ML STP dosimetry depends on training size: small samples (n=10) yielded poor performance (RMSE>85.98%, MAPE>89.1%). Accuracy improved significantly at n=500 (RMSE=14.07%) and plateaued beyond n=1,000 (peak RMSE=13.07%). Results indicate that the L-SVR model of the study requires sample sizes of n>200, with optimal gains up to n=2,000. This study suggests synthetic data as a methodological bridge between limited clinical datasets and data-intensive ML approaches.
Co-Authors Abdurrahman Aziz Wicaksono Ade Riana Assyifa Rahman Hakim Fira Dwi Ananda Indra Budiansah Jaja Muhammad Jabar M Dlorifun Naqiyyun M Zaidi Anwari Nur Atikah Rahmatul Hanifah Rien Ritawidya Supriyanto Ardjo Pawiro Syahril Siregar