All Outputs (6)

Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images (2022)
Journal Article
Quintero, P., Benoit, D., Cheng, Y., Moore, C., & Beavis, A. (2022). Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images. Physics in Medicine and Biology, 67(24), Article 245001. https://doi.org/10.1088/1361-6560/aca38a

Machine learning (ML) methods have been implemented in radiotherapy to aid virtual specific-plan verification protocols, predicting gamma passing rates (GPR) based on calculated modulation complexity metrics because of their direct relation to dose d... Read More about Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images.

Exploring hypoxic biology to improve radiotherapy outcomes (2022)
Journal Article
Li, C., Wiseman, L., Okoh, E., Lind, M., Roy, R., Beavis, A., & Monteiro dos Santos Pires, I. (2022). Exploring hypoxic biology to improve radiotherapy outcomes. Expert Reviews in Molecular Medicine, 24, Article E21. https://doi.org/10.1017/erm.2022.14

Ionising radiotherapy is a well-established, effective cancer treatment modality, whose efficacy has improved with the application of newer technological modalities. However, patient outcomes are governed and potentially limited by aspects of tumour... Read More about Exploring hypoxic biology to improve radiotherapy outcomes.

Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses (2021)
Journal Article
Tambe, N. S., Pires, I. M., Moore, C., Wieczorek, A., Upadhyay, S., & Beavis, A. W. (2021). Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses. Biomedical Physics and Engineering Express, 7(6), Article 065016. https://doi.org/10.1088/2057-1976/ac2635

Objectives. Volumetric modulated arc therapy (VMAT) allows for reduction of organs at risk (OAR) volumes receiving higher doses, but increases OAR volumes receiving lower radiation doses and can subsequently increasing associated toxicity. Therefore,... Read More about Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses.

Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients (2021)
Journal Article
Tambe, N. S., Pires, I. M., Moore, C., Wieczorek, A., Upadhyay, S., & Beavis, A. W. (2021). Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients. Biomedical Physics and Engineering Express, 7(6), https://doi.org/10.1088/2057-1976/ac1f94

Objectives. anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the plann... Read More about Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients.

Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator (2021)
Journal Article
Quintero, P., Cheng, Y., Benoit, D., Moore, C., & Beavis, A. (2021). Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator. British Journal of Radiology, 94(1122), Article 20201011. https://doi.org/10.1259/bjr.20201011

OBJECTIVE: High levels of beam modulation complexity (MC) and monitor units (MU) can compromise the plan deliverability of intensity-modulated radiotherapy treatments. Our study evaluates the effect of three treatment planning system (TPS) parameters... Read More about Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator.

Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy (2020)
Journal Article
Tambe, N., Pires, I. M., Moore, C., Cawthorne, C., & Beavis, A. (2020). Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy. British Journal of Radiology, 93(1106), https://doi.org/10.1259/bjr.20190535

Objectives: Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. T... Read More about Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy.