Evaluating the use of machine learning to predict expert-driven pareto-navigated calibrations for personalised automated radiotherapy planning

Foster, Iona, Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 and Wheeler, Philip 2023. Evaluating the use of machine learning to predict expert-driven pareto-navigated calibrations for personalised automated radiotherapy planning. Applied Sciences 13 (7) , 4548. 10.3390/app13074548

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Abstract

Automated planning (AP) uses common protocols for all patients within a cancer site. This work investigated using machine learning to personalise AP protocols for fully individualised planning. A ‘Pareto guided automated planning’ (PGAP) solution was used to generate patient-specific AP protocols and gold standard Pareto navigated reference plans (MCOgs) for 40 prostate cancer patients. Anatomical features related to geometry were extracted and two ML approaches (clustering and regression) that predicted patient-specific planning goal weights were trained on patients 1–20. For validation, three plans were generated for patients 21–40 using a standard site-specific AP protocol based on averaged weights (PGAPstd) and patient-specific AP protocols generated via regression (PGAP-MLreg) and clustering (PGAP-MLclus). The three methods were compared to MCOgs in terms of weighting factors and plan dose metrics. Results demonstrated that at the population level PGAPstd, PGAP-MLreg and PGAP-MLclus provided excellent correspondence with MCOgs. Deviations were either not statistically significant (p ≥ 0.05), or of a small magnitude, with all coverage and hotspot dose metrics within 0.2 Gy of MCOgs and OAR metrics within 0.7% and 0.4 Gy for volume and dose metrics, respectively. When compared to PGAPstd, patient-specific protocols offered minimal advantage for this cancer site, with both approaches highly congruent with MCOgs.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2076-3417
Date of First Compliant Deposit:	5 April 2023
Date of Acceptance:	27 March 2023
Last Modified:	12 May 2023 04:51
URI:	https://orca.cardiff.ac.uk/id/eprint/158382

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