Aplikasi Klinis AI dalam Treatment Planning Radioterapi untuk Brain Tumor
Abstract
Artificial Intelligence (AI) has emerged as an innovation that plays an important role in radiotherapy, especially in treatment planning. Artificial Intelligence (AI) has great potential to improve the effectiveness and safety of radiotherapy, particularly in treatment planning for brain tumor. The integration of Artificial Intelligence (AI)-based techniques in MRI imaging for radiotherapy planning is a significant advancement that improves the precision and effectiveness of brain cancer treatment. This review collected and analyzed 15 scientific articles, focusing on treatment planning to be analyzed through narrative synthesis to compare and abstract the results of each study to provide an overview of the contribution of AI in improving radiotherapy accuracy. The analysis showed that AI can improve the accuracy and efficiency of brain radiotherapy treatment planning, especially in the form of automatic segmentation, fMRI and DTI data processing. AI enables improvements in brain cancer treatment by optimizing dose planning and reducing radiation exposure to healthy tissue, contributing to more precise therapy and better patient outcomes.
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References
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[7] T. Stanescu, J. Hans-sonke, P. Stavrev, and B. G. Fallone, “3T MR-based treatment planning for radiotherapy of brain lesions,” vol. 40, no. 2, pp. 125–132, 2006.
[8] S. Wahyuni and L. Amalia, “Perkembangan Dan Prinsip Kerja Computed Tomography (CT Scan),” Galen. J. Kedokt. dan Kesehat. Mhs. Malikussaleh, vol. 1, no. 2, p. 88, 2022, doi: 10.29103/jkkmm.v1i2.8097.
[9] F. Basyid and K. Adi, “Segmentasi Citra Medis Untuk Pengenalan Objek Kanker Menggunakan Metode Active Contour,” Youngster Phys. J., vol. 3, no. 3, pp. 209–216, 2014.
[10] M. Hasan Fadlun and U. Hayati, “Jurnal Informatika dan Rekayasa Perangkat Lunak Klasifikasi Tumor Otak menggunakan Convolutional Neural Network dan Transfer Learning,” vol. 6, no. 1, pp. 289–295, 2024.
[11] Hasran, “Klasifikasi Penyakit Jantung Menggunakan Metode K-Nearest Neighbor,” Indones. J. Data Sci., vol. 1, no. 1, pp. 6–10, 2020, [Online]. Available: http://bit.ly/datasetcardio.
[12] D. S. Tsang et al., “A pilot study of machine-learning based automated planning for primary brain tumours,” Radiat. Oncol., vol. 17, no. 1, pp. 1–6, 2022, doi: 10.1186/s13014-021-01967-3.
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[14] F. Liu, P. Yadav, A. M. Baschnagel, and A. B. McMillan, “MR-based treatment planning in radiation therapy using a deep learning approach,” J. Appl. Clin. Med. Phys., vol. 20, no. 3, pp. 105–114, 2019, doi: 10.1002/acm2.12554.
[15] F. Putz et al., “The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning,” arXiv Prepr. , pp. 1–13, 2023, [Online]. Available: https://arxiv.org/abs/2304.07875v1
[16] Y. Wang et al., “Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture,” Quant. Imaging Med. Surg., vol. 14, no. 7, pp. 4475–4489, 2024, doi: 10.21037/qims-23-1627.
[17] D. LaBella et al., “Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation,” pp. 1–13, 2024, [Online]. Available: https://arxiv.org/abs/2405.18383v2
[18] S. J. Van Hoof, P. V. Granton, and F. Verhaegen, “Development and validation of a treatment planning system for small animal radiotherapy: SmART-Plan,” Radiother. Oncol., vol. 109, no. 3, pp. 361–366, 2013, doi: 10.1016/j.radonc.2013.10.003.
[19] J. Trusheim et al., “A state-of-the-art review and guidelines for tumor treating fields treatment planning and patient follow-up in glioblastoma,” CNS Oncol., vol. 6, no. 1, pp. 29–43, 2017, doi: 10.2217/cns-2016-0032.
[20] V. Kearney, J. W. Chan, G. Valdes, T. D. Solberg, and S. S. Yom, “The application of artificial intelligence in the IMRT planning process for head and neck cancer,” Oral Oncol., vol. 87, no. October, pp. 111–116, 2018, doi: 10.1016/j.oraloncology.2018.10.026.
[21] I. S. Ardan and R. Indraswari, “Sistem Berbasis Deep Learning untuk Segmentasi dan Klasifikasi Tingkat Keganasan Tumor Otak Menggunakan Citra MRI 3D,” vol. 6, no. 2, pp. 1–10, 2024.
[22] A. M. Aldawsari, B. Al-Qaisieh, D. A. Broadbent, D. Bird, L. Murray, and R. Speight, “The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review,” Phys. Imaging Radiat. Oncol., vol. 27, no. July, p. 100476, 2023, doi: 10.1016/j.phro.2023.100476.
[23] F. Citra R, F. Indriyani, and I. R. Rahadjeng, “Klasifikasi Tumor Otak Berbasis Magnetic Resonance Imaging Menggunakan Algoritma Convolutional Neural Network,” Digit. Transform. Technol., vol. 3, no. 2, pp. 918–924, 2024, doi: 10.47709/digitech.v3i2.3469.
[24] R. Andre, B. Wahyu, and R. Purbaningtyas, “Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Dengan Arsitektur Efficientnet-B3,” J. IT, vol. 11, no. 3, pp. 55–59, 2021, [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/index
[2] A. W. Beavis, P. Gibbs, R. A. Dealey, and V. J. Whitton, “Radiotherapy treatment planning of brain tumours using MRI alone,” Br. J. Radiol., vol. 71, no. MAY, pp. 544–548, 1998, doi: 10.1259/bjr.71.845.9691900.
[3] K. K. Ramesh et al., “A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking,” Cancers (Basel)., vol. 15, no. 15, 2023, doi: 10.3390/cancers15153956.
[4] K. V. Shiny, “Brain Tumor Segmentation and Classification Using Optimized U-Net,” J. Mech. Med. Biol., vol. 24, no. 1, 2024, doi: 10.1142/S0219519423500501.
[5] P. Metcalfe et al., “The potential for an enhanced role for MRI in radiation-therapy treatment planning,” Technol. Cancer Res. Treat., vol. 12, no. 5, pp. 429–446, 2013, doi: 10.7785/tcrt.2012.500342.
[6] M. A. Schmidt and G. S. Payne, “Radiotherapy planning using MRI,” Phys. Med. Biol., vol. 60, no. 22, pp. R323–R361, 2015, doi: 10.1088/0031-9155/60/22/R323.
[7] T. Stanescu, J. Hans-sonke, P. Stavrev, and B. G. Fallone, “3T MR-based treatment planning for radiotherapy of brain lesions,” vol. 40, no. 2, pp. 125–132, 2006.
[8] S. Wahyuni and L. Amalia, “Perkembangan Dan Prinsip Kerja Computed Tomography (CT Scan),” Galen. J. Kedokt. dan Kesehat. Mhs. Malikussaleh, vol. 1, no. 2, p. 88, 2022, doi: 10.29103/jkkmm.v1i2.8097.
[9] F. Basyid and K. Adi, “Segmentasi Citra Medis Untuk Pengenalan Objek Kanker Menggunakan Metode Active Contour,” Youngster Phys. J., vol. 3, no. 3, pp. 209–216, 2014.
[10] M. Hasan Fadlun and U. Hayati, “Jurnal Informatika dan Rekayasa Perangkat Lunak Klasifikasi Tumor Otak menggunakan Convolutional Neural Network dan Transfer Learning,” vol. 6, no. 1, pp. 289–295, 2024.
[11] Hasran, “Klasifikasi Penyakit Jantung Menggunakan Metode K-Nearest Neighbor,” Indones. J. Data Sci., vol. 1, no. 1, pp. 6–10, 2020, [Online]. Available: http://bit.ly/datasetcardio.
[12] D. S. Tsang et al., “A pilot study of machine-learning based automated planning for primary brain tumours,” Radiat. Oncol., vol. 17, no. 1, pp. 1–6, 2022, doi: 10.1186/s13014-021-01967-3.
[13] A. A. K. Abdel Razek et al., “Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging,” Insights Imaging, vol. 12, no. 1, 2021, doi: 10.1186/s13244-021-01102-6.
[14] F. Liu, P. Yadav, A. M. Baschnagel, and A. B. McMillan, “MR-based treatment planning in radiation therapy using a deep learning approach,” J. Appl. Clin. Med. Phys., vol. 20, no. 3, pp. 105–114, 2019, doi: 10.1002/acm2.12554.
[15] F. Putz et al., “The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning,” arXiv Prepr. , pp. 1–13, 2023, [Online]. Available: https://arxiv.org/abs/2304.07875v1
[16] Y. Wang et al., “Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture,” Quant. Imaging Med. Surg., vol. 14, no. 7, pp. 4475–4489, 2024, doi: 10.21037/qims-23-1627.
[17] D. LaBella et al., “Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation,” pp. 1–13, 2024, [Online]. Available: https://arxiv.org/abs/2405.18383v2
[18] S. J. Van Hoof, P. V. Granton, and F. Verhaegen, “Development and validation of a treatment planning system for small animal radiotherapy: SmART-Plan,” Radiother. Oncol., vol. 109, no. 3, pp. 361–366, 2013, doi: 10.1016/j.radonc.2013.10.003.
[19] J. Trusheim et al., “A state-of-the-art review and guidelines for tumor treating fields treatment planning and patient follow-up in glioblastoma,” CNS Oncol., vol. 6, no. 1, pp. 29–43, 2017, doi: 10.2217/cns-2016-0032.
[20] V. Kearney, J. W. Chan, G. Valdes, T. D. Solberg, and S. S. Yom, “The application of artificial intelligence in the IMRT planning process for head and neck cancer,” Oral Oncol., vol. 87, no. October, pp. 111–116, 2018, doi: 10.1016/j.oraloncology.2018.10.026.
[21] I. S. Ardan and R. Indraswari, “Sistem Berbasis Deep Learning untuk Segmentasi dan Klasifikasi Tingkat Keganasan Tumor Otak Menggunakan Citra MRI 3D,” vol. 6, no. 2, pp. 1–10, 2024.
[22] A. M. Aldawsari, B. Al-Qaisieh, D. A. Broadbent, D. Bird, L. Murray, and R. Speight, “The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review,” Phys. Imaging Radiat. Oncol., vol. 27, no. July, p. 100476, 2023, doi: 10.1016/j.phro.2023.100476.
[23] F. Citra R, F. Indriyani, and I. R. Rahadjeng, “Klasifikasi Tumor Otak Berbasis Magnetic Resonance Imaging Menggunakan Algoritma Convolutional Neural Network,” Digit. Transform. Technol., vol. 3, no. 2, pp. 918–924, 2024, doi: 10.47709/digitech.v3i2.3469.
[24] R. Andre, B. Wahyu, and R. Purbaningtyas, “Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Dengan Arsitektur Efficientnet-B3,” J. IT, vol. 11, no. 3, pp. 55–59, 2021, [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/index
Published
2024-12-23
How to Cite
TANDIBALI, Hesty; RAHMANIA, Siti; PUTRI, Erlinda Ratnasari.
Aplikasi Klinis AI dalam Treatment Planning Radioterapi untuk Brain Tumor.
Progressive Physics Journal, [S.l.], v. 5, n. 2, p. 458-468, dec. 2024.
ISSN 2722-7707.
Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/ppj/article/view/1442>. Date accessed: 14 jan. 2025.
doi: https://doi.org/10.30872/ppj.v5i2.1442.
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
