Development of personalised optimisation of advanced-stage non-small cell lung cancer patients with volumetric modulated arc radiotherapy

Tambe, Nilesh Suresh

Abstract

Background and Aim of the study: Lung cancer is the third most common cancer in the UK. A significant number of these patients are diagnosed with inoperable advanced-stage non-small cell lung cancer. Until recently, the standard of care for these patients was radiotherapy with or without chemotherapy but the overall survival remained poor, with a 5-year survival of only 13%. Recent studies showed significant improvement in overall
survival in patients who are suitable for, and have received, immunotherapy, in addition to chemotherapy and radiotherapy. Radiotherapy aims to deliver tumoricidal doses to the target volume whilst minimising doses to the surrounding organs at risk (OAR). However, achieving this goal could be challenging especially when treating advanced-stage tumours, as it could increase OAR doses and increase toxicities to an unacceptable level. Furthermore, several factors affect the achieved dose distribution including, patient’s geometry, treatment technique, planner’s experience, beam geometry, optimisation parameters, and interventions used during treatments. It is therefore important to develop methods to personalise treatment plan optimisation for advanced-stage non-small cell lung cancer (NSCLC) patients to achieve minimum OAR doses without compromising target doses using patient-specific parameters. This study aims to develop knowledge-based planning models (KBP) using patient-specific factors to
determine personalised treatment planning optimisation for advanced-stage NSCLC patients treated with volumetric modulated arc therapy (VMAT) to reduce OAR doses whilst delivering intended doses to the target volume.
Methods: Four KBP models were developed using patient-specific dose and volume parameters to predict minimum achievable OAR doses, identify optimal arc parameters, trigger adaptive radiotherapy and estimate doses to adapted gross tumour volume using patients’ geometry. The KBP models were verified using independent patient data sets. Change in treatment plan optimisation could increase modulation and affect plan
deliverability therefore, several modulation indexes were calculated and plans were measured on the clinical linear accelerator to assess the effect of change in optimisation on treatment plan delivery.
Results: The KBP models developed showed that relatively simple models can predict OAR doses and arc parameters and help identify patients for adaptive radiotherapy. The models can accurately estimate personalised and progressive dose escalation. The KBP resulted in a significant reduction in plan variability in all three studied dosimetric parameters, volume of lungs receiving 5Gy (V5), 20Gy (V20) and mean lung dose (MLD)
by 4.9% (p=0.007, 10.8% to 5.9%), 1.3% (p=0.038, 4.0% to 2.7%) and 0.9Gy (p=0.012, 2.5Gy to 1.6Gy), respectively. The individualised arc geometry resulted in a significant reduction in lungs (V5 = - 15.1%, MLD = - 1.0Gy) and heart (MHD = - 1.4Gy) doses without compromising target coverage. The models, which were developed to predict changes in PTV coverage (∆𝑉95𝑃𝑇𝑉) using a specific biomarker (Programmed death-ligand 1 (PD-L1 expression)) and the difference in ‘planning’ and ‘fraction’ planning target volume (PTV) centre of the mass (characterised by mean square difference, MSD), could predict change in PTV coverage within ± 1.0% for 77% of the total fractions. Furthermore, the models developed for predicting personalised and progressive dose escalation predicted doses within 0.4% and 0.7% respectively. Additionally, the plan complexity and deliverability measurements show that plan complexity could increase but may not affect treatment delivery significantly.
Conclusion: The studies performed show that altering the ‘standard’ treatment planning optimisation approach could significantly reduce OAR doses and improve target coverage. This will help reduce toxicities and improve local control and overall survival and outcome for inoperable advanced-stage NSCLC patients.

Citation

Tambe, N. S. (2022). Development of personalised optimisation of advanced-stage non-small cell lung cancer patients with volumetric modulated arc radiotherapy. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4224655

• Thesis Type: Thesis
• Deposit Date: Jan 20, 2023
• Publicly Available Date: Feb 24, 2023
• Keywords: Biomedical sciences
• Public URL: https://hull-repository.worktribe.com/output/4224655
• Additional Information: Department of Biomedical Sciences, The University of Hull
• Award Date: Jan 1, 2022