An Overview of the Development of Stereotactic Body Radiation Therapy
DOI:
https://doi.org/10.54097/09nIy12xKeywords:
Stereotactic Body Radiotherapy, Radiation Biology, Key Technology, Main Equipment, Development Tendency, Computer Vision, Artificial Intelligence (AI)Abstract
Stereotactic body radiation therapy (SBRT) refers to focusing high-energy rays in three-dimensional space on the tumor lesion area, reducing the dose received by surrounding normal tissues, which can effectively improve the local control rate of the tumor and reduce the probability of complications.With the comprehensive development of medical imaging, radiation biology and other disciplines, this less-fractional, high-dose radiotherapy method has been increasingly developed and applied in clinical practice. The background, radio-biological basis, key technologies and main equipment of SBRT are discussed, and its future development direction is prospected.
Downloads
References
DeSantis, C. E., Miller, K. D., Goding Sauer, A., Jemal, A., Siegel, R. L. (2019). Cancer statistics for African Americans, 2019. CA: A Cancer Journal for Clinicians, 69(3), 211-233.
Xu, L., Wang, J., Huang, X., Liu, H., Wang, Y., & Peng, W. (2021). Dose assessment of stereotactic body radiation therapy for primary liver cancer based on 4D-CT image sets. Scientific Reports, 11(1), 1-8.
Hulshof, M. C. C. M., Andratschke, N., Barriger, R. B., Belderbos, J. S. A., Cuijpers, J. P., Dahele, M., ... & Yu, J. Q. (2019). SBRT in NSCLC: rationale, current evidence, controversies and recommendations. Nature Reviews Clinical Oncology, 16(4), 233-245.
Pan, C. C., Liang, J. A., Hung, Y. C., Yen, Y. C., Wang, W. Y., Huang, M. Y., ... & Lin, W. C. (2020). Dose escalation with a simultaneous integrated boost technique for patients with non-small cell lung cancer receiving stereotactic body radiotherapy. Scientific Reports, 10(1), 1-10.
Zhao, B., Deng, L., Liu, L., Wang, X., & Li, X. (2021). Effectiveness and safety of stereotactic body radiotherapy for the treatment of spinal metastases: A systematic review and meta-analysis. Cancer Medicine, 10(2), 423-434.
Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278-280.
Ahn, S. H., Kim, T. H., Han, Y., & Park, S. Y. (2021). Machine learning-based radiomics for predicting treatment response and overall survival of patients with gastric cancer. Scientific Reports, 11(1), 1-12.
Chen, C. C., Yang, H. J., & Liu, S. H. (2020). An intelligent computing model for improving accuracy in lung cancer radiotherapy. Computers in Biology and Medicine, 126, 104032.
Xiao, Y., Jin, J. Y., Zhang, Y., Yom, S. S., & Song, Y. (2020). Computer vision-based real-time tumor tracking for proton pencil beam scanning radiation therapy. Physics in Medicine & Biology, 65(15), 155003.
Wang, Z., Zha, H., Shi, J., & Chen, H. (2018, December). Thermal and Stress Analysis of an X-ray Target for 6 MeV Medical Linear Accelerators. In 9th International Particle Accelerator Conference (pp. 572-574).
Peng, M., Zha, H., Shi, J., Gai, W., & Wang, Z. (2018). Development of an Half-Cell Accelerating Structure in Tsinghua. Proc. IPAC'18, 4023-4025.
Jiang, Y., Shi, J., Wang, P., Zha, H., Wang, Z., Wu, X., ... & Gai, W. (2018, June). Design and measurement of the X-band pulse compressor for TTX. In Proc. 9th Int. Part. Accel. Conf.(IPAC) (pp. 4745-4748).
Wang, Z., Shi, J., Zha, H., & Liu, J. (2019). Dose Measurement Experiments for Single and Composite Targets in 6 Mev Linear Accelerators.
Wang, Z., Zha, H., Liu, Z., & Shi, J. (2019). DAMAGE BEHAVIOR OF TUNGSTEN TARGETS FOR 6 MeV LINEAR ACCELERATORS. Target (mA), 160(158), 159.
Meng, X., Zha, H., Shi, J., Zheng, S., Chen, H., Wang, Z., ... & Liu, Y. (2018). Investigation of Transverse Wakefield and Beam Break Up Effect in Irradiation Linacs.
Wang, P., Shi, J., Zha, H., Cao, D., Wang, Z., & Chen, H. (2018). RF System Based on Two Klystrons and Phase Modulation for Photo-Cathode Injector.
Wang, P., SHI, J., ZHA, H., CAO, D., Wang, Z., & CHEN, H. (2018, June). Fabrication and Cold Test of the Correction Cavity Chain for Klystron-Based CLIC. In 9th Int. Particle Accelerator Conf.(IPAC'18), Vancouver, BC, Canada, April 29-May 4, 2018 (pp. 4014-4016). JACOW Publishing, Geneva, Switzerland.
Liu, J., Zha, H., Shi, J., Qiu, J., Chen, H., Wu, X., & Wang, Z. (2018, June). High-Power Test of a Compact X-Band RF Rotary Joint. In 9th Int. Particle Accelerator Conf.(IPAC'18), Vancouver, BC, Canada, April 29-May 4, 2018 (pp. 4017-4019). JACOW Publishing, Geneva, Switzerland.
Ping, W., Jiaru, S., Hao, Z., Dezhi, C., Zhihui, W., Cheng, C., & Huaibi, C. HIGH POWER TEST OF THE S-BAND SPHERICAL PULSE COMPRESSOR AT TSINGHUA UNIVERSITY.
Hulshof, M. C., Andratschke, N., Swinnen, A., Siva, S., Guckenberger, M., Le, P. E., ... & Glynne-Jones, R. (2019). Teaching radiation oncology in the age of artificial intelligence and personalised medicine. Radiotherapy and Oncology, 133, 10-14.
Pan, T., Zhao, L., Zhang, F., Dong, J., Zhu, J., Wang, H., & Zhao, J. (2020). Efficacy and safety of SBRT for liver metastases from colorectal cancer: a systematic review and meta-analysis. BMC Cancer, 20(1), 1-12.
Yang, C., Tian, X., Song, C., Wu, J., Zhang, Z., Jia, X., ... & Chen, M. (2021). Radiomics-based machine learning for predicting lung cancer recurrence after radiotherapy. Cancer Medicine, 10(1), 150-162.
Zhang, Z., Wang, J., Ma, X., Liu, G., Li, Y., & He, J. (2021). Liver tumor segmentation in CT images using a novel U-net-based deep learning network. Medical & Biological Engineering & Computing, 59(4), 821-836.
Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41.
Hu, H., Li, S., Huang, J., Liu, B., & Che, C. (2023). Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10), 42-46.
Tianbo, S., Weijun, H., Jiangfeng, C., Weijia, L., Quan, Y., & Kun, H. (2023, January). Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition. In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 834-837). IEEE.
Mou, C., Dai, W., Ye, X., & Wu, J. (2023, July). Research On Method Of User Preference Analysis Based on Entity Similarity and Semantic Assessment. In 2023 8th International Conference on Signal and Image Processing (ICSIP) (pp. 1029-1033). IEEE.
Dai, W., Mou, C., Wu, J., & Ye, X. (2023, May). Diabetic Retinopathy Detection with Enhanced Vision Transformers: The Twins-PCPVT Solution. In 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 403-407). IEEE. http:// www. halcyon. com/pub/journals/21ps03-vidmar.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution 4.0 International License.