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Wheeler PA, West NS, Powis R, Maggs R, Chu M, Pearson RA, Willis N, Kurec B, Reed KL, Lewis DG, Staffurth J, Spezi E, Millin AE. Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer. Radiat Oncol 2024; 19:45. [PMID: 38589961 PMCID: PMC11003074 DOI: 10.1186/s13014-024-02404-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/15/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer. METHODS The implemented 'Pareto Guided Automated Planning' (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a 'Protocol Based Automatic Iterative Optimisation' planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions' clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution. RESULTS PGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review. CONCLUSIONS PGAP enabled intuitive adaptation of automated protocols to an institution's planning aims and yielded plans more congruent with the institution's clinical preference than the locally produced manual clinical plans.
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Affiliation(s)
- Philip A Wheeler
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK.
| | - Nicholas S West
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Richard Powis
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Rhydian Maggs
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Michael Chu
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Rachel A Pearson
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne, UK
| | - Nick Willis
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Bartlomiej Kurec
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Katie L Reed
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - David G Lewis
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - John Staffurth
- School of Medicine, Cardiff University, Cardiff, Wales, UK
- Velindre Cancer Centre, Medical Directorate, Cardiff, Wales, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, UK
| | - Anthony E Millin
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
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Zeverino M, Piccolo C, Wuethrich D, Jeanneret-Sozzi W, Marguet M, Bourhis J, Bochud F, Moeckli R. Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning. Phys Imaging Radiat Oncol 2023; 28:100492. [PMID: 37780177 PMCID: PMC10534254 DOI: 10.1016/j.phro.2023.100492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/15/2023] [Accepted: 09/15/2023] [Indexed: 10/03/2023] Open
Abstract
Background and purpose Automation in radiotherapy treatment planning aims to improve both the quality and the efficiency of the process. The aim of this study was to report on a clinical implementation of a Deep Learning (DL) auto-planning model for left-sided breast cancer. Materials and methods The DL model was developed for left-sided breast simultaneous integrated boost treatments under deep-inspiration breath-hold. Eighty manual dose distributions were revised and used for training. Ten patients were used for model validation. The model was then used to design 17 clinical auto-plans. Manual and auto-plans were scored on a list of clinical goals for both targets and organs-at-risk (OARs). For validation, predicted and mimicked dose (PD and MD, respectively) percent error (PE) was calculated with respect to manual dose. Clinical and validation cohorts were compared in terms of MD only. Results Median values of both PD and MD validation plans fulfilled the evaluation criteria. PE was < 1% for targets for both PD and MD. PD was well aligned to manual dose while MD left lung mean dose was significantly less (median:5.1 Gy vs 6.1 Gy). The left-anterior-descending artery maximum dose was found out of requirements (median values:+5.9 Gy and + 2.9 Gy, for PD and MD respectively) in three validation cases, while it was reduced for clinical cases (median:-1.9 Gy). No other clinically significant differences were observed between clinical and validation cohorts. Conclusion Small OAR differences observed during the model validation were not found clinically relevant. The clinical implementation outcomes confirmed the robustness of the model.
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Affiliation(s)
- Michele Zeverino
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Consiglia Piccolo
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Diana Wuethrich
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Wendy Jeanneret-Sozzi
- Radiation Oncology Department, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Maud Marguet
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Jean Bourhis
- Radiation Oncology Department, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Francois Bochud
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Raphael Moeckli
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
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