Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy.

Published on Mar 1, 2021in Practical radiation oncology2.948
· DOI :10.1016/J.PRRO.2020.09.012
Roberta Castriconi6
Estimated H-index: 6
Giovanni Mauro Cattaneo29
Estimated H-index: 29
+ 8 AuthorsClaudio Fiorino54
Estimated H-index: 54
Abstract Purpose To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation. Methods and Materials One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose–volume parameters and by blinded clinical evaluation of 3 expert clinicians. Results KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum–PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P Conclusions The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.
#1Roberta CastriconiH-Index: 6
#2C. FiorinoH-Index: 7
Last. Riccardo CalandrinoH-Index: 32
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Abstract Purpose To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer. Material and methods The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the ...
6 CitationsSource
#1Kevin L. Moore (UCSD: University of California, San Diego)H-Index: 44
The “treatment planning” component of managing a radiotherapy patient currently consumes hours, even days, of human effort. The time and workforce demands of the current planning paradigm can expose patients to delays and potentially substandard treatments, all while standing as seemingly insurmountable roadblocks to adaptive radiotherapy. Automating the treatment planning process is not a new idea, but recent advances have shown that automated planning might finally be turning the corner from n...
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#1Tiziana RancatiH-Index: 27
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The treatment of a patient with radiation therapy is planned to find the optimal way to treat a tumour while minimizing the dose received by the surrounding normal tissues. In order to better exploit the possibilities of this process, the availability of accurate and quantitative knowledge of the peculiar responses of the different tissues is of paramount importance. This book provides an invaluable tutorial for radiation oncologists, medical physicists, and dosimetrists involved in the planning...
#1Yaorong Ge (UNCC: University of North Carolina at Charlotte)H-Index: 21
#2Q. Jackie Wu (Duke University)H-Index: 25
PURPOSE: Intensity-Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge-intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are ac...
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#1Livia MarrazzoH-Index: 22
#2Icro Meattini (UniFI: University of Florence)H-Index: 22
Last. Stefania Pallotta (UniFI: University of Florence)H-Index: 21
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#1Roberta CastriconiH-Index: 6
#2C. FiorinoH-Index: 7
Last. Giovanni Mauro CattaneoH-Index: 29
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Abstract Purpose To develop and apply a stepping approach for the validation of Knowledge-based (KB) models for planning optimization: the method was applied to the case of concomitant irradiation of pelvic nodes and prostate + seminal−vesicles bed irradiation in post-prostatectomy patients. Methods The clinical VMAT plans of 52 patients optimized by two reference planners were selected to generate a KB-model (RapidPlan, v.13.5 Varian). A stepping-validation approach was followed by comparing KB...
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#1Eliane Miguel-Chumacero (Beatson West of Scotland Cancer Centre)H-Index: 2
#2Garry Currie (Beatson West of Scotland Cancer Centre)H-Index: 4
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#1Elisabetta Cagni (Cardiff University)H-Index: 12
#2Andrea BottiH-Index: 10
Last. Ben J.M. Heijmen (EUR: Erasmus University Rotterdam)H-Index: 64
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#1A. ScaggionH-Index: 9
#2M. FusellaH-Index: 7
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#1Mikoto Tamura (Kindai University)H-Index: 8
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Cited By0
#1E. Costa (Curie Institute)H-Index: 5
#2Thomas Richir (Curie Institute)
Last. Ludovic De Marzi (Université Paris-Saclay)H-Index: 10
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INTRODUCTION The aim of this study was to evaluate the performance of a knowledge-based planning (KBP) model for breast cancer trained on plans performed on a conventional linac with 6 MV FF (flattening filter) beams and volumetric-modulated arc therapy (VMAT) for plans performed on the new jawless Halcyon© system with 6 MV FFF (flattening filter-free) beams. MATERIALS AND METHODS Based on the RapidPlan© (RP) KBP optimization engine, a DVH Estimation Model was first trained using 56 VMAT left-si...