Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality

Paolo Caricato; Sara Trivellato; Roberto Pellegrini; Gianluca Montanari; Martina Camilla Daniotti; Bianca Bordigoni; Valeria Faccenda; Denis Panizza; Sofia Meregalli; Elisa Bonetto; Peter Voet; Stefano Arcangeli; Elena De Ponti

doi:10.1007/s12672-023-00800-5

Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality

Discov Oncol. 2023 Sep 30;14(1):180. doi: 10.1007/s12672-023-00800-5.

Authors

Paolo Caricato^{1

2}, Sara Trivellato³, Roberto Pellegrini⁴, Gianluca Montanari³, Martina Camilla Daniotti^{3

5}, Bianca Bordigoni^{3

6}, Valeria Faccenda^{3

5}, Denis Panizza^{3

7}, Sofia Meregalli^{7

8}, Elisa Bonetto⁸, Peter Voet⁹, Stefano Arcangeli^{7

8}, Elena De Ponti^{3

7}

Affiliations

¹ Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy. paolo.caricato93@gmail.com.
² Department of Physics, University of Milan, Milan, Italy. paolo.caricato93@gmail.com.
³ Medical Physics Department, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy.
⁴ Medical Affairs, Elekta AB, Stockholm, Sweden.
⁵ Department of Physics, University of Milan, Milan, Italy.
⁶ Department of Physics, University of Milano Bicocca, Milan, Italy.
⁷ School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy.
⁸ Department of Radiation Oncology, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy.
⁹ Research Clinical Liaison, Elekta AB, Stockholm, Sweden.

Abstract

Background: To investigate the capability of a not-yet commercially available fully automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), to further improve the plan quality of an already-validated Wish List (WL) pushing on the organs-at-risk (OAR) sparing without compromising target coverage and plan delivery accuracy.

Material and methods: Twenty-four mono-institutional consecutive cervical cancer Volumetric-Modulated Arc Therapy (VMAT) plans delivered between November 2019 and April 2022 (50 Gy/25 fractions) have been retrospectively selected. In mCycle the LO planning algorithm was combined with the a-priori multi-criterial optimization (MCO). Two versions of WL have been defined to reproduce manual plans (WL01), and to improve the OAR sparing without affecting minimum target coverage and plan delivery accuracy (WL02). Robust WLs have been tuned using a subset of 4 randomly selected patients. The remaining plans have been automatically re-planned by using the designed WLs. Manual plans (MP) and mCycle plans (mCP01 and mCP02) were compared in terms of dose distributions, complexity, delivery accuracy, and clinical acceptability. Two senior physicians independently performed a blind clinical evaluation, ranking the three competing plans. Furthermore, a previous defined global quality index has been used to gather into a single score the plan quality evaluation.

Results: The WL tweaking requests 5 and 3 working days for the WL01 and the WL02, respectively. The re-planning took in both cases 3 working days. mCP01 best performed in terms of target coverage (PTV V_95% (%): MP 98.0 [95.6-99.3], mCP01 99.2 [89.7-99.9], mCP02 96.9 [89.4-99.5]), while mCP02 showed a large OAR sparing improvement, especially in the rectum parameters (e.g., Rectum D_50% (Gy): MP 41.7 [30.2-47.0], mCP01 40.3 [31.4-45.8], mCP02 32.6 [26.9-42.6]). An increase in plan complexity has been registered in mCPs without affecting plan delivery accuracy. In the blind comparisons, all automated plans were considered clinically acceptable, and mCPs were preferred over MP in 90% of cases. Globally, automated plans registered a plan quality score at least comparable to MP.

Conclusions: This study showed the flexibility of the Lexicographic approach in creating more demanding Wish Lists able to potentially minimize toxicities in RT plans.

Keywords: Automated planning; Cervical cancer; Lexicographic optimization; Plan comparison; Plan quality; VMAT.