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Sihono, D. S. K.
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Radiation Dose Prediction for Cervical Cancer Patients Using IMRT Technique with a Machine Learning Model Based on Support Vector Regression (SVR) Mushaddaq, R. F.; Sihono, D. S. K.; Prajitno, P.
Atom Indonesia Vol 50, No 3 (2024): DECEMBER 2024
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2024.1483

Abstract

Cervical cancer poses significant global health challenges, necessitating the need for innovative treatment approaches. This study addresses the gap in current radiotherapy methods by integrating Support Vector Regression (SVR) to predict radiation doses for cervical cancer treatment, thereby enhancing the precision of Intensity Modulated Radiation Therapy (IMRT). Using datasets from 102 and 173 cervical cancer cases, we developed and validated an SVR model to predict dose distributions based on radiomic and dosiomic features. The model demonstrated strong performance, achieving a Mean Absolute Error (MAE) of 0.069 for the testing data, with specific performance metrics as follows: bladder mean dose MAE of 0.0693, bowel mean dose MAE of 0.0926, and rectum mean dose MAE of 0.0779. These findings highlight the potential of machine learning to refine radiotherapy planning, reduce the workload on medical physicists, and improve patient outcomes. Future research should focus on expanding dataset sizes and enhancing model precision, particularly for anatomically challenging regions.
Verification of Breast Cancer Treatment Planning with Various Radiation Techniques Using Monte Carlo Simulations and Linac Log Files Sugandi, R. D.; Azzi, A.; Fadli, M.; Sihono, D. S. K.
Atom Indonesia Vol 51, No 3 (2025): DECEMBER 2025
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2025.1618

Abstract

Due to the complexity of radiotherapy techniques, rigorous Patient-Specific Quality Assurance (PSQA) is crucial to ensure the accuracy of treatment plans. This study aims to evaluate the performance of the Treatment Planning System (TPS) by comparing its dose distribution calculations with those obtained from the PRIMO Monte Carlo simulation. Treatment plans for 3D-CRT, IMRT, and VMAT were generated for a Rando breast phantom using the TPS. Subsequently, the dose distributions from the TPS were compared with those obtained from the PRIMO Monte Carlo simulation. Key metrics, including Homogeneity Index (HI) and Conformity Index (CI), were calculated to assess the quality of dose distribution. Furthermore, the dose constraints on OARs were evaluated to assess the impact on surrounding healthy tissues. To further validate the TPS, dose distributions from the linac log file (Dynalog) for VMAT were reconstructed within the PRIMO environment. These reconstructed distributions were then compared with the dose distributions calculated directly by the TPS. Gamma index analysis was employed to evaluate the agreement between these two sets of data. The comparison between TPS and Monte Carlo simulations revealed that 3D-CRT plans exhibited smaller deviations in HI and CI compared to IMRT and VMAT plans. However, a significant improvement in HI and CI values was observed in both IMRT planning simulations and Dynalog VMAT file simulations, indicating enhanced plan quality. The dose received by OARs in all treatment plans remained within the acceptable dose thresholds, demonstrating effective sparing of surrounding healthy tissues. For the PSQA procedure, the 3D-CRT technique is still the safest due to its lower level of complexity compared to IMRT and VMAT. More complex treatments should consider the robustness of treatment transfer information from TPS to linac to avoid dosimetry errors.