References

[1] Thompson, R. F., et al. (2018). "Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?" Radiotherapy and Oncology, 129(3), 421-426. [Link]
Cited for: Overview of treatment planning workflows and time requirements in radiation oncology (Section on clinical workflows discussing iterative planning processes)

[2] Hanna, T. P., et al. (2020). "Mortality due to cancer treatment delay: Systematic review and meta-analysis." The BMJ, 371, m4087. [Link]
Cited for: Impact of treatment delays on patient outcomes (Meta-analysis results: "Four week delay in treatment associated with increased mortality across seven cancer types")

[3] Vandewinckele, L., et al. (2020). "Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance." Radiotherapy and Oncology, 153, 55-66. [Link]
Cited for: Challenges in AI implementation and workflow integration in radiotherapy (Section 4 on implementation challenges and quality assurance requirements)

[4] Sharp, G., et al. (2014). "Vision 20/20: Perspectives on automated image segmentation for radiotherapy." Medical Physics, 41(5), 050902. [Link]
Cited for: Inter-observer variability in manual contouring (Section III discussing consistency challenges in organ delineation across multiple observers)

[5] Huynh, E., et al. (2020). "Artificial Intelligence in Radiation Oncology." Nature Reviews Clinical Oncology, 17(12), 771-781. [Link]
Cited for: Clinical impact of contouring variability on treatment outcomes (Discussion of how segmentation variations affect dose distributions and treatment efficacy)

[6] Luk, S. M. H., et al. (2022). "Improving the Quality of Care in Radiation Oncology using Artificial Intelligence." Radiotherapy and Oncology, 166, 74-84. [Link]
Cited for: Time requirements for manual quality assurance processes (Section 3 discussing QA workflow bottlenecks and resource allocation)

[7] Wang, C., et al. (2019). "Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future." Technology in Cancer Research & Treatment, 18, 1533033819873922. [Link]
Cited for: Limitations of traditional QA approaches (Section on quality assurance discussing error detection rates in manual review processes)

[8] Meyer, P., et al. (2018). "Survey on deep learning for radiotherapy." Computers in Biology and Medicine, 98, 126-146. [Link]
Cited for: Challenges in deploying AI across different institutions (Section 5.3 on domain adaptation and generalization issues in multi-center settings)

[9] Santoro, M., et al. (2022). "Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond." Applied Sciences, 12(7), 3223. [Link]
Cited for: Economic and resource implications of extended planning workflows (Discussion of cost-effectiveness and resource utilization in radiotherapy planning)