Automation and artificial intelligence in radiation therapy treatment planning

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI-based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher-quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety.</p

Quantification of the geometric uncertainty when using implanted markers as a surrogate for lung tumor motion

Background: Fiducial markers are used as surrogates for tumor location during radiation therapy treatment. Developments in lung fiducial marker and implantation technology have provided a means to insert markers endobronchially for tracking of lung tumors. This study quantifies the surrogacy uncertainty (SU) when using endobronchially implanted markers as a surrogate for lung tumor position. Methods: We evaluated SU for 17 patients treated in a prospective electromagnetic-guided MLC tracking trial. Tumor and markers were segmented on all phases of treatment planning 4DCTs and all frames of pretreatment kilovoltage fluoroscopy acquired from lateral and frontal views. The difference in tumor and marker position relative to end-exhale position was calculated as the SU for both imaging methods and the distributions of uncertainties analyzed. Results: The mean (range) tumor motion amplitude in the 4DCT scan was 5.9 mm (1.7–11.7 mm) in the superior–inferior (SI) direction, 2.2 mm (0.9–5.5 mm) in the left–right (LR) direction, and 3.9 mm (1.2–12.9 mm) in the anterior–posterior (AP) direction. Population-based analysis indicated symmetric SU centered close to 0 mm, with maximum 5th/95th percentile values over all axes of −2.0 mm/2.1 mm with 4DCT, and −2.3/1.3 mm for fluoroscopy. There was poor correlation between the SU measured with 4DCT and that measured with fluoroscopy on a per-patient basis. We observed increasing SU with increasing surrogate motion. Based on fluoroscopy analysis, the mean (95% CI) SU was 5% (2%–8%) of the motion magnitude in the SI direction, 16% (6%–26%) of the motion magnitude in the LR direction, and 33% (23%–42%) of the motion magnitude in the AP direction. There was no dependence of SU on marker distance from the tumor. Conclusion: We have quantified SU due to use of implanted markers as surrogates for lung tumor motion. Population 95th percentile range are up to 2.3 mm, indicating the approximate contribution of SU to total geometric uncertainty. SU was relatively small compared with the SI motion, but substantial compared with LR and AP motion. Due to uncertainty in estimations of patient-specific SU, it is recommended that population-based margins are used to account for this component of the total geometric uncertainty

Quantification of the geometric uncertainty when using implanted markers as a surrogate for lung tumor motion

Background: Fiducial markers are used as surrogates for tumor location during radiation therapy treatment. Developments in lung fiducial marker and implantation technology have provided a means to insert markers endobronchially for tracking of lung tumors. This study quantifies the surrogacy uncertainty (SU) when using endobronchially implanted markers as a surrogate for lung tumor position. Methods: We evaluated SU for 17 patients treated in a prospective electromagnetic-guided MLC tracking trial. Tumor and markers were segmented on all phases of treatment planning 4DCTs and all frames of pretreatment kilovoltage fluoroscopy acquired from lateral and frontal views. The difference in tumor and marker position relative to end-exhale position was calculated as the SU for both imaging methods and the distributions of uncertainties analyzed. Results: The mean (range) tumor motion amplitude in the 4DCT scan was 5.9 mm (1.7–11.7 mm) in the superior–inferior (SI) direction, 2.2 mm (0.9–5.5 mm) in the left–right (LR) direction, and 3.9 mm (1.2–12.9 mm) in the anterior–posterior (AP) direction. Population-based analysis indicated symmetric SU centered close to 0 mm, with maximum 5th/95th percentile values over all axes of −2.0 mm/2.1 mm with 4DCT, and −2.3/1.3 mm for fluoroscopy. There was poor correlation between the SU measured with 4DCT and that measured with fluoroscopy on a per-patient basis. We observed increasing SU with increasing surrogate motion. Based on fluoroscopy analysis, the mean (95% CI) SU was 5% (2%–8%) of the motion magnitude in the SI direction, 16% (6%–26%) of the motion magnitude in the LR direction, and 33% (23%–42%) of the motion magnitude in the AP direction. There was no dependence of SU on marker distance from the tumor. Conclusion: We have quantified SU due to use of implanted markers as surrogates for lung tumor motion. Population 95th percentile range are up to 2.3 mm, indicating the approximate contribution of SU to total geometric uncertainty. SU was relatively small compared with the SI motion, but substantial compared with LR and AP motion. Due to uncertainty in estimations of patient-specific SU, it is recommended that population-based margins are used to account for this component of the total geometric uncertainty