A Knowledge-Based Planning model for IMRT in breast and lung cancer
Main Article Content
Abstract
Objective: The advent of Knowledge-Based Planning (KBP) models has introduced a transformative approach to Intensity-Modulated Radiation Therapy (IMRT) treatment planning in breast cancer and lung cancer cases. This paper explores the application of KBP models to these specific cancer types, highlighting their potential to enhance treatment accuracy, efficiency, and patient outcomes.
Methods: By leveraging historical treatment data and machine learning techniques, KBP-IMRT offers a data-driven framework for optimizing dose distributions, minimizing radiation exposure to healthy tissues, and improving overall treatment plan quality.
Results: Through a comprehensive review of the literature and clinical case studies, this paper underscores the advantages of KBP-IMRT, such as streamlined planning processes and improved plan consistency, while acknowledging the challenges associated with model development and implementation.
Conclusion: As the field of radiotherapy continues to evolve, KBP models hold the promise of shaping the future of personalized and precise cancer treatment strategies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
- Webb S. Intensity-Modulated Radiation Therapy. Published online May 6, 2015. https://doi.org/10.1201/9781420034110
- Sun YS, Zhao Z, Yang ZN, et al. Risk Factors and Preventions of Breast Cancer. International Journal of Biological Sciences. 2017, 13(11): 1387-1397. https://doi.org/10.7150/ijbs.21635
- Schabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer Epidemiology, Biomarkers & Prevention. 2019, 28(10): 1563-1579. https://doi.org/10.1158/1055-9965.epi-19-0221
- Ge Y, Wu QJ. Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches. Medical Physics. 2019, 46(6): 2760-2775. https://doi.org/10.1002/mp.13526
- Baskar R, Lee KA, Yeo R, et al. Cancer and Radiation Therapy: Current Advances and Future Directions. International Journal of Medical Sciences. 2012, 9(3): 193-199. https://doi.org/10.7150/ijms.3635
- Dantzig GB. Linear Programming. Operations Research. 2002, 50(1): 42-47. https://doi.org/10.1287/opre.50.1.42.17798
- Kelley CT. Iterative Methods for Optimization. Published online January 1999. https://doi.org/10.1137/1.9781611970920
- Henderson BW, Busch TM, Snyder JW. Fluence rate as a modulator of PDT mechanisms. Lasers in Surgery and Medicine. 2006, 38(5): 489-493. https://doi.org/10.1002/lsm.20327
- Garraway LA, Verweij J, Ballman KV. Precision Oncology: An Overview. Journal of Clinical Oncology. 2013, 31(15): 1803-1805. https://doi.org/10.1200/jco.2013.49.4799
- Drzymala RE, Mohan R, Brewster L, et al. Dose-volume histograms. International Journal of Radiation Oncology*Biology*Physics. 1991, 21(1): 71-78. https://doi.org/10.1016/0360-3016(91)90168-4
- Alexopoulos EC. Introduction to multivariate regression analysis. Hippokratia. 2010, 14(Suppl 1): 23-28.
- Bonaccorso G. Machine learning algorithms. Packt Publishing Ltd, 2017.
- Speirs CK, DeWees TA, Rehman S, et al. Heart Dose Is an Independent Dosimetric Predictor of Overall Survival in Locally Advanced Non–Small Cell Lung Cancer. Journal of Thoracic Oncology. 2017, 12(2): 293-301. https://doi.org/10.1016/j.jtho.2016.09.134
- Krueger EA, Fraass BA, Pierce LJ. Clinical aspects of intensity-modulated radiotherapy in the treatment of breast cancer. Seminars in Radiation Oncology. 2002, 12(3): 250-259. https://doi.org/10.1053/srao.2002.32468
- Kuhl CK. MRI of breast tumors. European Radiology. 2000, 10(1): 46-58. https://doi.org/10.1007/s003300050006
- Abdou Y, Gupta M, Asaoka M, et al. Left sided breast cancer is associated with aggressive biology and worse outcomes than right sided breast cancer. Scientific Reports. 2022, 12(1). https://doi.org/10.1038/s41598-022-16749-4
- Hoffmann L, Knap MM, Alber M, et al. Optimal beam angle selection and knowledge-based planning significantly reduces radiotherapy dose to organs at risk for lung cancer patients. Acta Oncologica. 2020, 60(3): 293-299. https://doi.org/10.1080/0284186x.2020.1856409
- Feuvret L, Noël G, Mazeron JJ, et al. Conformity index: A review. International Journal of Radiation Oncology*Biology*Physics. 2006, 64(2): 333-342. https://doi.org/10.1016/j.ijrobp.2005.09.028
- Kataria T, Sharma K, Subramani V, et al. Homogeneity Index: An objective tool for assessment of conformal radiation treatments. Journal of Medical Physics. 2012, 37(4): 207. https://doi.org/10.4103/0971-6203.103606