Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Wheeler, Paul S.; Chu, Michael; Holmes, Rosemary A.; Woodley, O.; Jones, Ceri S.; Maggs, R.; Staffurth, John; Palaniappan, Nachi; Spezi, Emiliano; Lewis, G. Malcolm; Campbell, Sue; Fitzgibbon, Jim; Millin, Anthony

doi:10.1016/j.radonc.2019.08.001

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…To the authors' knowledge only another study has been published in this clinical setting. Wheeler et al (40) evaluated a novel automated planning solution whose Pareto navigation-based methodology enabled clinical decision-making on trade-off balancing to be incorporated within automated protocols. The authors successfully applied their engine to prostate cancer patients with and without elective nodal irradiation and robustly generated high quality plans in an efficient manner.…”

Section: Discussionmentioning

confidence: 99%

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

et al. 2021

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BackgroundIn radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle3 for full planning automation of VMAT prostate cancer treatments.Material and MethodsThirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans.ResultsFor similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification.ConclusionsThe Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.

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Section: Discussionmentioning

confidence: 99%

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

et al. 2021

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“…The proposed method to explore trade-offs in planning goals has some similarities with the well-known Pareto navigation, using a graphical user interface with sliders to find a clinically favorable plan (8,(26)(27)(28)(29)(30)(31). Also in that method, multiple plans are automatically generated for manual plan selection.…”

Section: Discussionmentioning

confidence: 99%

Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity—A Proof of Principle Study for Prostate Cancer

et al. 2020

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Background: Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs between planning aims, using a set of treatment plans with a-priori defined (slightly) different balances. To this purpose, we developed an automated workflow and explored its use for prostate cancer. Materials and Methods: For each of the 50 study patients, seven plans were generated, including the so-called clinical plan, with currently clinically desired ≥99% dose coverage for the low-dose planning target volume (PTV Low). The six other plans were generated with different, reduced levels of PTV Low coverage, aiming at reductions in rectum dose and consequently in predicted grade≥2 late gastro-intestinal (GI) normal tissue complication probabilities (NTCPs), while keeping other dosimetric differences small. The applied NTCP model included diabetes as a non-dosimetric predictor. All plans were generated with a clinically applied, in-house developed algorithm for automated multi-criterial plan generation. Results: With diabetes, the average NTCP reduced from 24.9 ± 4.5% for ≥99% PTV Low coverage to 17.3 ± 2.6% for 90%, approaching the NTCP (15.4 ± 3.0%) without diabetes and full PTV Low coverage. Apart from intended differences in PTV Low coverage and rectum dose, other differences between the clinical plan and the six alternatives were indeed minor. Obtained NTCP reductions were highly patient-specific (ranging from 14.4 to 0.1%), depending on patient anatomy. Even for patients with equal NTCPs in the clinical plan, large differences were found in NTCP reductions. Conclusions: A clinically feasible workflow has been proposed for systematic exploration of patient-specific trade-offs between various treatment aims. For each patient, automated planning is used to generate a limited set of treatment plans with Bijman et al. Automated Planning for Trade-Off Exploration well-defined variations in the balances between the aims. For prostate cancer, trade-offs between PTV Low coverage and predicted GI NTCP were explored. With relatively small coverage reductions, significant NTCP reductions could be obtained, strongly depending on patient anatomy. Coverage reductions could also make up for enhanced NTCPs related to diabetes as co-morbidity, again dependent on the patient. The proposed system can play an important role in further personalization of patient care.

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“…Information of this kind could prove valuable for automatic treatment plan selection. Combining the Pareto optimality concept with auto planning does provide not only a well optimized plan, based on the quality of the other plans in the database, but an objectively optimal plan on the Pareto front [20,21]. In addition, the mathematical definition of the decision problem could include decision maker preferences in such a way as to lead the search towards clinically preferred regions.…”

Section: Discussionmentioning

confidence: 99%

Exploration of clinical preferences in treatment planning of radiotherapy for prostate cancer using Pareto fronts and clinical grading analysis

Kyroudi

Petersson

Ozsahin

et al. 2020

Physics and Imaging in Radiation Oncology

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Introduction: Radiotherapy treatment planning is a multi-criteria problem. Any optimization of the process produces a set of mathematically optimal solutions. These optimal plans are considered mathematically equal, but they differ in terms of the trade-offs involved. Since the various objectives are conflicting, the choice of the best plan for treatment is dependent on the preferences of the radiation oncologists or the medical physicists (decision makers).We defined a clinically relevant area on a prostate Pareto front which better represented clinical preferences and determined if there were differences among radiation oncologists and medical physicists. Methods and materials: Pareto fronts of five localized prostate cancer patients were used to analyze and visualize the trade-off between the rectum sparing and the PTV under-dosage. Clinical preferences were evaluated with Clinical Grading Analysis by asking nine radiation oncologists and ten medical physicists to rate pairs of plans presented side by side. A choice of the optimal plan on the Pareto front was made by all decision makers. Results:The plans in the central region of the Pareto front (1-4% PTV under-dosage) received the best evaluations. Radiation oncologists preferred the organ at risk (OAR) sparing region (2.5-4% PTV under-dosage) while medical physicists preferred better PTV coverage (1-2.5% PTV under-dosage). When the Pareto fronts were additionally presented to the decisions makers they systematically chose the plan in the trade-off region (0.5-1% PTV under-dosage). Conclusion:We determined a specific region on the Pareto front preferred by the radiation oncologists and medical physicists and found a difference between them.

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Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Cited by 10 publications

References 38 publications

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity—A Proof of Principle Study for Prostate Cancer

Exploration of clinical preferences in treatment planning of radiotherapy for prostate cancer using Pareto fronts and clinical grading analysis

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