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Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1%${D}_{1{\mathrm{\% }}}$ and D98%${D}_{98{\mathrm{\% }}}$ were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization. [ABSTRACT FROM AUTHOR]

Purpose: A novel compensator‐based system has been proposed which delivers intensity‐modulated radiation therapy (IMRT) with cobalt‐60 beams. This could improve access to advanced radiotherapy in low‐ and middle‐income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low‐ and middle‐income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). Methods: The novel treatment device considered here employs a cobalt‐60 source and nine compensators. Each compensator is produced by 3‐D printing a thin plastic mold which is then filled on‐demand within the machine with reusable 2‐mm‐diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in‐plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator‐based cobalt‐60 IMRT plans were created for 46 head‐and‐neck cancer cases and compared to the linac‐based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam‐on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. Results: The differences in percent depth doses and in‐plane profiles between the Eclipse and Monte Carlo simulations were 0.65% ± 0.41% and 1.02% ± 0.99%, respectively, and the 80%–20% penumbra agreed within 0.46 ± 0.27 mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were >95% (3%/3 mm criteria). In 46 clinical head‐and‐neck cases, the cobalt‐60 compensator‐based IMRT plans had planning target volume (PTV) coverages similar to linac‐based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt‐60 compensator‐based IMRT plans versus linac‐based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43 ± 1.92, 3.39 ± 4.67, and 2.40 ± 3.71 Gy, while those for the rest of the organs were <1 Gy. The average beam‐on time per field was 0.42 ± 0.10 min for the 6 MV multi‐leaf‐collimator plans while those for the cobalt‐60 compensator plans were 0.17 ± 0.01 and 0.31 ± 0.01 min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in‐plane profiles from measurements and Monte Carlo simulations, which differences are 1.34 ± 1.90% and 0.13 ± 2.16% for two different fields. Conclusions: A novel compensator‐based IMRT system using cobalt‐60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac‐based plans in all test cases with shorter estimated beam‐on times. This system enables reliable, high‐quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web‐based platform for auto‐contouring and auto‐planning. [ABSTRACT FROM AUTHOR]

Purpose: To fully automate CT‐based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. Methods: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4‐field‐box (4‐field‐box), 3D conformal radiotherapy (3D‐CRT), and volumetric modulated arc therapy (VMAT). These auto‐planning algorithms were combined with a previously developed auto‐contouring system. To improve the quality of the 4‐field‐box and 3D‐CRT plans, we used an in‐house, field‐in‐field (FIF) automation program. Thirty‐five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5‐point Likert scale. Results: Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4‐field‐box, 3D‐CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. Conclusion: Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource‐constrained radiotherapy departments in low‐ and middle‐income countries. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the dosimetric impact of multi‐leaf collimator (MLC) positioning errors on a Varian Halcyon for both random and systematic errors, and to evaluate the effectiveness of portal dosimetry quality assurance in catching clinically significant changes caused by these errors. Both random and systematic errors were purposely added to 11 physician‐approved head and neck volumetric modulated arc therapy (VMAT) treatment plans, yielding a total of 99 unique plans. Plans were then delivered on a preclinical Varian Halcyon linear accelerator and the fluence was captured by an opposed portal dosimeter. When comparing dose–volume histogram (DVH) values of plans with introduced MLC errors to known good plans, clinically significant changes to target structures quickly emerged for plans with systematic errors, while random errors caused less change. For both error types, the magnitude of clinically significant changes increased as error size increased. Portal dosimetry was able to detect all systematic errors, while random errors of ±5 mm or less were unlikely to be detected. Best detection of clinically significant errors, while minimizing false positives, was achieved by following the recommendations of AAPM TG‐218. Furthermore, high‐ to moderate correlation was found between dose DVH metrics for normal tissues surrounding the target and portal dosimetry pass rates. Therefore, it may be concluded that portal dosimetry on the Halcyon is robust enough to detect errors in MLC positioning before they introduce clinically significant changes to VMAT treatment plans. [ABSTRACT FROM AUTHOR]