Literature DB >> 32676685

The role of a knowledge based dose-volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients : Training and validation of a novel commercial system.

Luca Cozzi1,2, Reynald Vanderstraeten3, Antonella Fogliata4, Feng-Ling Chang5, Po-Ming Wang5.   

Abstract

PURPOSE: To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients.
METHODS: A cohort of 65 patients was retrospectively selected: 50 were used to "train" the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 cm3 or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial.
RESULTS: There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose-volume objectives were met on average for all structures except for D0.5 cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: D99% to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; D0.5 cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions.
CONCLUSION: A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results.

Entities:  

Keywords:  Hepatocellular cancer; Intensity-modulated proton therapy; Machine learning; RapidArc; Volumetric modulated arc therapy

Mesh:

Year:  2020        PMID: 32676685     DOI: 10.1007/s00066-020-01664-2

Source DB:  PubMed          Journal:  Strahlenther Onkol        ISSN: 0179-7158            Impact factor:   3.621


  1 in total

1.  Evaluation of an Automated Proton Planning Solution.

Authors:  Alexander R Delaney; Wilko F Verbakel; Jari Lindberg; Timo K Koponen; Ben J Slotman; Max Dahele
Journal:  Cureus       Date:  2018-12-06
  1 in total
  3 in total

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

Authors:  Yihang Xu; Nellie Brovold; Jonathan Cyriac; Elizabeth Bossart; Kyle Padgett; Michael Butkus; Tejan Diwanj; Adam King; Alan Dal Pra; Matt Abramowitz; Alan Pollack; Nesrin Dogan
Journal:  Int J Part Ther       Date:  2021-06-15

2.  Knowledge-Based Planning for Robustly Optimized Intensity-Modulated Proton Therapy of Head and Neck Cancer Patients.

Authors:  Yihang Xu; Jonathan Cyriac; Mariluz De Ornelas; Elizabeth Bossart; Kyle Padgett; Michael Butkus; Tejan Diwanji; Stuart Samuels; Michael A Samuels; Nesrin Dogan
Journal:  Front Oncol       Date:  2021-10-19       Impact factor: 6.244

3.  Training and validation of a knowledge-based dose-volume histogram predictive model in the optimisation of intensity-modulated proton and volumetric modulated arc photon plans for pleural mesothelioma patients.

Authors:  Davide Franceschini; Luca Cozzi; Antonella Fogliata; Beatrice Marini; Luciana Di Cristina; Luca Dominici; Ruggero Spoto; Ciro Franzese; Pierina Navarria; Tiziana Comito; Giacomo Reggiori; Stefano Tomatis; Marta Scorsetti
Journal:  Radiat Oncol       Date:  2022-08-26       Impact factor: 4.309

  3 in total

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