Automated algorithm for calculation of setup corrections and planning target volume margins for offline image‐guided radiotherapy protocols

Abubakar, Auwal; Zamri, Nada Alia M; Shaukat, Shazril Imran; Zin, Hafiz M.

doi:10.1002/acm2.13291

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Studies from LMICs using AI/ML to assist in online adaptive planning, treatment delivery, and QA are few and far between, and more work in this area must be encouraged. 64,[66][67][68]…”

Section: Data Annotation Radiomics and Response Predictionmentioning

confidence: 99%

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Krishnamurthy

Mummudi

Goda

et al. 2022

JCO Global Oncology

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The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.

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“…Studies from LMICs using AI/ML to assist in online adaptive planning, treatment delivery, and QA are few and far between, and more work in this area must be encouraged. 64,[66][67][68]…”

Section: Data Annotation Radiomics and Response Predictionmentioning

confidence: 99%

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Krishnamurthy

Mummudi

Goda

et al. 2022

JCO Global Oncology

View full text Add to dashboard Cite

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“…In the treatment chain errors and uncertainties can occur that affect the accuracy of the delivered dose to the patient. Experience shows that setup and organ motion uncertainties are a major source of deviations during EIVD verification procedures routinely practiced [19] , [20] , [21] . These uncertainties can mainly be attributed to the use of the immobilization devices, correction strategies, patient body habitus and expertise of RTTs.…”

Section: Introductionmentioning

confidence: 99%

Comparing treatment uncertainty for ultra- vs. standard-hypofractionated breast radiation therapy based on in-vivo dosimetry

Fiagan

Bossuyt

Machiels

et al. 2022

Physics and Imaging in Radiation Oncology

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Assessment of using an Octavius 4D Measuring System for Patient-specific VMAT Quality Assurance in Togo

Yawo AC,

Kodjo JF,

Adama

et al. 2024

J Radiol Oncol

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Purpose and objective: Quality assurance (QA) programs are designed to improve the quality and the safety of radiation treatments, including machine- and patient-specific QA (PSQA). The objective of this study was to evaluate the current state of PSQA practice and identify the area for potential improvement for VMAT delivery. Materials and methods: The Octavius 4D (O4D) system accuracy was evaluated using an O4D homogeneous phantom for different field sizes. The system response to dose linearity, field sizes, and PDD difference tests were performed against the calculated dose of the treatment planning system (TPS) for a 6 MV photon beam. The deviation of the delivered dose was evaluated at the isocenter and different depths. Moreover, pretreatment verification of 40 VMAT plans was performed including prostate cancer (PC), head and neck cancer (HNC), uterine and cervical cancer (UCC), and breast cancer (BC). The PTW VeriSoft software was used to perform the local and global 3D gamma analysis by comparing the reconstructed 3D dose against the calculated dose using criteria 2%/2 mm and 3%/3 mm, 20% of low-dose threshold, and 95% of gamma passing rate (%GP) tolerance level. In the clinical scenario, the sensitivity of the O4D system in detecting VMAT delivery and setup errors has been investigated by measuring the variation of %GP values before and after the simulated errors using one of the VMAT plans related to each treatment site. Results: The O4D system reported good agreement for linearity, field size, and PDD differences with TPS dose being within ± 2% tolerance for a 6 MV photon beam. Output factors were consistent between the ionization chamber and the O4D detector 1500 array down to 4 x 4 cm2 field size with a maximum deviation of less than 1%. The introduction of deliberate errors caused the decrease of %GP values. In most scenarios, the %GP value of simulated errors was detected with 2%/2 mm and ranged between the detection threshold and gamma passing threshold. Conclusion: The results indicate that the O4D system is sensitive to detect delivery and setup errors with restrictive criteria of 2%/2 mm for routine pretreatment verification. Moreover, this system should be used in combination with kV-CBCT to improve dosimetry accuracy and treatment reproducibility.

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Automated algorithm for calculation of setup corrections and planning target volume margins for offline image‐guided radiotherapy protocols

Cited by 3 publications

References 43 publications

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Comparing treatment uncertainty for ultra- vs. standard-hypofractionated breast radiation therapy based on in-vivo dosimetry

Assessment of using an Octavius 4D Measuring System for Patient-specific VMAT Quality Assurance in Togo

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