Radiation Dose Prediction for Cervical Cancer Patients Using IMRT Technique with a Machine Learning Model Based on Support Vector Regression (SVR)
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Radiation Dose Prediction for Cervical Cancer Patients Using IMRT Technique with a Machine Learning Model Based on Support Vector Regression (SVR) |
| 2. | Creator | Author's name, affiliation, country | R. F. Mushaddaq; University of Indonesia, Jl. Lingkar, Pondok Cina, Beji, Depok City, West Java, 16424, Indonesia |
| 2. | Creator | Author's name, affiliation, country | D. S. K. Sihono; University of Indonesia, Jl. Lingkar, Pondok Cina, Beji, Depok City, West Java, 16424, Indonesia; Indonesia |
| 2. | Creator | Author's name, affiliation, country | P. Prajitno; University of Indonesia, Jl. Lingkar, Pondok Cina, Beji, Depok City, West Java, 16424, Indonesia; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Cervical cancer; Support Vector Regression (SVR); Radiotherapy planning; Machine learning; Intensity Modulated Radiation Therapy (IMRT) |
| 4. | Description | 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. |
| 5. | Publisher | Organizing agency, location | National Research and Innovation Agency |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-11-25 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://atomindonesia.brin.go.id/index.php/aij/article/view/1483 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.55981/aij.2024.1483 |
| 11. | Source | Title; vol., no. (year) | Atom Indonesia; Vol 50, No 3 (2024): DECEMBER 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Atom Indonesia This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. |