Literature DB >> 33449952

Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions.

Maria Rago1, Lorenzo Placidi1,2, Mattia Polsoni3,4, Giulia Rambaldi3,4, Davide Cusumano2, Francesca Greco2, Luca Indovina2, Sebastiano Menna2, Elisa Placidi2, Gerardina Stimato2, Stefania Teodoli2, Gian Carlo Mattiucci2, Silvia Chiesa2, Fabio Marazzi2, Valeria Masiello2, Vincenzo Valentini1,2, Marco De Spirito1,2, Luigi Azario1,2.   

Abstract

PURPOSE: To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions. METHODS AND MATERIALS: Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures.
RESULTS: All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average.
CONCLUSIONS: All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.

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Year:  2021        PMID: 33449952      PMCID: PMC7810311          DOI: 10.1371/journal.pone.0245305

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  74 in total

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7.  Multi-institutional evaluation of knowledge-based planning performance of volumetric modulated arc therapy (VMAT) for head and neck cancer.

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Authors:  Mari H B Hjelstuen; Ingvil Mjaaland; Johan Vikström; Kjell Ivar Dybvik
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Authors:  Indra J Das; Chee-Wai Cheng; Kashmiri L Chopra; Raj K Mitra; Shiv P Srivastava; Eli Glatstein
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10.  Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution.

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  1 in total

1.  Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.

Authors:  Pier Giorgio Esposito; Roberta Castriconi; Paola Mangili; Sara Broggi; Andrei Fodor; Marcella Pasetti; Alessia Tudda; Nadia Gisella Di Muzio; Antonella Del Vecchio; Claudio Fiorino
Journal:  Phys Imaging Radiat Oncol       Date:  2022-06-23
  1 in total

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