Correction to: Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer

Krayenbuehl, J.; Zamburlini, M.; Ghandour, S.; Pachoud, M.; Tanadini‐Lang, Stephanie; Tol, Jim P.; Gückenberger, Matthias; Verbakel, Wilko F.A.R.

doi:10.1186/s13014-018-1176-x

Cited by 2 publications

(1 citation statement)

References 1 publication

(1 reference statement)

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“…Such skilled human inputs have also been reported in the numerous studies applying these engines on diverse clinical inverse optimization problems. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]22,[27][28][29] Interestingly, Janssen et al applied the DVH estimation for a plan quality audit on clinical AP plans that had been manually refined after AP if deemed necessary and identified some suboptimal plans that could be further improved. 12 This study indicated that they may not always produce Pareto-optimal plans, despite the success of these automated planning engines in producing plans at or near clinical acceptance and driving the DVHs for OARs beyond the specified dose goals.…”

Section: Discussionmentioning

confidence: 99%

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition

Smith

Granatowicz

Stoltenberg

et al. 2019

Technol Cancer Res Treat

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Purpose: Pinnacle Auto-Planning and Eclipse RapidPlan are 2 major commercial automated planning engines that are fundamentally different: Auto-Planning mimics real planners in the iterative optimization, while RapidPlan generates static dose objectives from estimations predicted based on a prior knowledge base. This study objectively compared their performances on intensity-modulated radiotherapy planning for prostate fossa and lymphatics adopting the plan quality metric used in the 2011 American Association of Medical Dosimetrists Plan Challenge. Methods: All plans used an identical intensity-modulated radiotherapy beam setup and a simultaneous integrated boost prescription (68 Gy/56 Gy to prostate fossa/lymphatics). Auto-Planning was used to retrospectively plan on 20 patients, which were subsequently employed as the library to build an RapidPlan model. To compare the 2 engines’ performances, a test set including 10 patients and the Plan Challenge patient was planned by both Auto-Planning (master) and RapidPlan (student) without manual intervention except for a common dose normalization and evaluated using the plan quality metric that included 14 quantitative submetrics ranging over target coverage, spillage, and organ at risk doses. Plan quality metric scores were compared between the Auto-Planning and RapidPlan plans using the Mann-Whitney U test. Results: There was no significant difference between the overall performance of the 2 engines on the 11 test cases ( P = .509). Among the 14 submetrics, Auto-Planning and RapidPlan showed no significant difference on most submetrics except for 2. On the Plan Challenge case, Auto-Planning scored 129.9 and RapidPlan scored 130.3 out of 150, as compared with the average score of 116.9 ± 16.4 (range: 58.2-142.5) among the 125 Plan Challenge participants. Conclusion: Using an innovative study design, an objective comparison has been conducted between 2 major commercial automated inverse planning engines. The 2 engines performed comparably with each other and both yielded plans at par with average human planners. Using a constant-performing planner (Auto-Planning) to train and to compare, RapidPlan was found to yield plans no better than but as good as its library plans.

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Section: Discussionmentioning

confidence: 99%

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition

Smith

Granatowicz

Stoltenberg

et al. 2019

Technol Cancer Res Treat

View full text Add to dashboard Cite

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Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy

Brovold

Cyriac

et al. 2021

International Journal of Particle Therapy

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Purpose To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results The resulting R2 (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ2 test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (−1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (−0.67Gy ± 2.17Gy) and lower integral dose (−0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (−1.13 ± 2.34). Conclusions The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites.

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Correction to: Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer

Cited by 2 publications

References 1 publication

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition

Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy

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