ArticleRadiation oncology (London, England)2018
Automated VMAT planning for postoperative adjuvant treatment of advanced gastric cancer.
Article in Radiation oncology (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 23 citations in OpenAlex.
- Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
- Automatic Radiobiological Comparison of Radiation Therapy Plans: An Application to Gastric Cancer.Cancers · 2022Article
- Evaluation of an automated template-based treatment planning system for radiotherapy of anal, rectal and prostate cancer.Technical innovations & patient support in radiation oncology · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Efficacy and toxicity of capecitabine combined with intensity-modulated radiotherapy after D1/D2 lymph node dissection in patients with gastric cancer.World journal of gastrointestinal oncology · 2021Article
- Utilisation of Pareto navigation techniques to calibrate a fully automated radiotherapy treatment planning solution.Physics and imaging in radiation oncology · 2019Article
- Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.Technology in cancer research & treatment · 2019Review
- Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.The British journal of radiology · 2018Review
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative/adjuvant radiotherapy of advanced gastric cancer involves a large planning target volume (PTV) with multi-concave shapes which presents a challenge for volumetric modulated arc therapy (VMAT) planning. This study investigates the advantages of automated VMAT planning for this site compared to manual VMAT planning by expert planners.
methodsFor 20 gastric cancer patients in the postoperative/adjuvant setting, dual-arc VMAT plans were generated using fully automated multi-criterial treatment planning (autoVMAT), and compared to manually generated VMAT plans (manVMAT). Both automated and manual plans were created to deliver a median dose of 45 Gy to the PTV using identical planning and segmentation parameters. Plans were evaluated by two expert radiation oncologists for clinical acceptability. AutoVMAT and manVMAT plans were also compared based on dose-volume histogram (DVH) and predicted normal tissue complication probability (NTCP) analysis.
resultsBoth manVMAT and autoVMAT plans were considered clinically acceptable. Target coverage was similar (manVMAT: 96.6 ± 1.6%, autoVMAT: 97.4 ± 1.0%, p = 0.085). With autoVMAT, median kidney dose was reduced on average by > 25%; (for left kidney from 11.3 ± 2.1 Gy to 8.9 ± 3.5 Gy (p = 0.002); for right kidney from 9.2 ± 2.2 Gy to 6.1 ± 1.3 Gy (p < 0.001)). Median dose to the liver was lower as well (18.8 ± 2.3 Gy vs. 17.1 ± 3.6 Gy, p = 0.048). In addition, Dmax of the spinal cord was significantly reduced (38.3 ± 3.7 Gy vs. 31.6 ± 2.6 Gy, p < 0.001). Substantial improvements in dose conformity and integral dose were achieved with autoVMAT plans (4.2% and 9.1%, respectively; p < 0.001). Due to the better OAR sparing in the autoVMAT plans compared to manVMAT plans, the predicted NTCPs for the left and right kidney and the liver-PTV were significantly reduced by 11.3%, 12.8%, 7%, respectively (p ≤ 0.001). Delivery time and total number of monitor units were increased in autoVMAT plans (from 168 ± 19 s to 207 ± 26 s, p = 0.006) and (from 781 ± 168 MU to 1001 ± 134 MU, p = 0.003), respectively.
conclusionsFor postoperative/adjuvant radiotherapy of advanced gastric cancer, involving a complex target shape, automated VMAT planning is feasible and can substantially reduce the dose to the kidneys and the liver, without compromising the target dose delivery.
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