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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

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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.

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Author information

Authors and Affiliations

  1. Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Center, IRCSS, Via Manzoni 56, 20089, Milan-Rozzano, Italy

    Luca Cozzi & Antonella Fogliata

  2. Department of Biomedical Sciences, Humanitas University, Rozzano, Italy

    Luca Cozzi

  3. Varian Medical Systems, Palo Alto, CA, USA

    Reynald Vanderstraeten

  4. Radiation Oncology Department, Asian University Hospital, Taichung, Taiwan, Province of China

    Feng-Ling Chang & Po-Ming Wang

Authors
  1. Luca Cozzi
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  2. Reynald Vanderstraeten
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  3. Antonella Fogliata
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  4. Feng-Ling Chang
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  5. Po-Ming Wang
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Corresponding author

Correspondence to Luca Cozzi.

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Conflict of interest

L. Cozzi, who is Clinical Research Scientist at Humanitas Cancer Center, acts as Scientific Advisor to Varian Medical Systems. R. Vanderstraeten is senior product manager for proton treatment planning at Varian Medical Systems. A. Fogliata, F.-L. Chang and P.-M. Wang declare that they have no competing interests.

Caption Electronic Supplementary Material

66_2020_1664_MOESM1_ESM.docx

Supplementary materials contains explanatory figure about the principal component analysys; additional figure about the model training; comparative figure for the average DVH for more OARs; the side analysis for small vs large lesions.

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Cozzi, L., Vanderstraeten, R., Fogliata, A. et al. The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients. Strahlenther Onkol 197, 332–342 (2021). https://doi.org/10.1007/s00066-020-01664-2

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  • DOI: https://doi.org/10.1007/s00066-020-01664-2

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