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Bioengineering
Volume 12
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10.3390/bioengineering12080897
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans

by
Simone Buzzi
1,2,
Pietro Mancosu
1,3,*,
Andrea Bresolin
1,
Pasqualina Gallo
1,
Francesco La Fauci
1,
Francesca Lobefalo
1,
Lucia Paganini
1,
Marco Pelizzoli
1,
Giacomo Reggiori
1,
Ciro Franzese
1,3,
Stefano Tomatis
1,
Marta Scorsetti
1,3,
Cristina Lenardi
2,4 and
Nicola Lambri
1,5
1
IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
2
Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, 20133 Milan, Italy
3
Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
4
Milano Division, National Institute for Nuclear Physics, 20133 Milan, Italy
5
Scuola di Specializzazione di Fisica Medica, Università degli Studi di Milano, 20133 Milan, Italy
*
Author to whom correspondence should be addressed.
Bioengineering 2025, 12(8), 897; https://doi.org/10.3390/bioengineering12080897 (registering DOI)
Submission received: 29 July 2025 / Revised: 15 August 2025 / Accepted: 18 August 2025 / Published: 21 August 2025
(This article belongs to the Special Issue Radiation Imaging and Therapy for Biomedical Engineering)
Versions Notes

Abstract

Stereotactic radiosurgery (SRS) for multiple brain metastases can be delivered with a single isocenter and non-coplanar arcs, achieving highly conformal dose distributions at the cost of extreme modulation of treatment machine parameters. As a result, SRS plans are at a higher risk of patient-specific quality assurance (PSQA) failure compared to standard treatments. This study aimed to develop a machine-learning (ML) model to predict the PSQA outcome (gamma passing rate, GPR) of SRS plans. Five hundred and ninety-two consecutive patients treated between 2020 and 2024 were selected. GPR analyses were performed using a 3%/1 mm criterion and a 95% action limit for each arc. Fifteen plan complexity metrics were used as input features to predict the GPR of an arc. A stratified and a time-series approach were employed to split the data into training (1555 arcs), validation (389 arcs), and test (486 arcs) sets. The ML model achieved a mean absolute error of 2.6% on the test set, with a 0.83% median residual value (measured/predicted). Lower values of the measured GPR tended to be overestimated. Sensitivity and specificity were 93% and 56%, respectively. ML models for virtual QA of SRS can be integrated into clinical practice, facilitating more efficient PSQA approaches.
Keywords: machine learning; patient-specific QA; stereotactic radiosurgery; HyperArc; radiotherapy machine learning; patient-specific QA; stereotactic radiosurgery; HyperArc; radiotherapy

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MDPI and ACS Style

Buzzi, S.; Mancosu, P.; Bresolin, A.; Gallo, P.; La Fauci, F.; Lobefalo, F.; Paganini, L.; Pelizzoli, M.; Reggiori, G.; Franzese, C.; et al. A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans. Bioengineering 2025, 12, 897. https://doi.org/10.3390/bioengineering12080897

AMA Style

Buzzi S, Mancosu P, Bresolin A, Gallo P, La Fauci F, Lobefalo F, Paganini L, Pelizzoli M, Reggiori G, Franzese C, et al. A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans. Bioengineering. 2025; 12(8):897. https://doi.org/10.3390/bioengineering12080897

Chicago/Turabian Style

Buzzi, Simone, Pietro Mancosu, Andrea Bresolin, Pasqualina Gallo, Francesco La Fauci, Francesca Lobefalo, Lucia Paganini, Marco Pelizzoli, Giacomo Reggiori, Ciro Franzese, and et al. 2025. "A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans" Bioengineering 12, no. 8: 897. https://doi.org/10.3390/bioengineering12080897

APA Style

Buzzi, S., Mancosu, P., Bresolin, A., Gallo, P., La Fauci, F., Lobefalo, F., Paganini, L., Pelizzoli, M., Reggiori, G., Franzese, C., Tomatis, S., Scorsetti, M., Lenardi, C., & Lambri, N. (2025). A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans. Bioengineering, 12(8), 897. https://doi.org/10.3390/bioengineering12080897

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