ESTRO 2023 - Abstract Book

S2043

Digital Posters

ESTRO 2023

The aim of this study is to propose a predictive model able to calculate the probability of obtain suboptimal fractions starting from the defined bandwidth.

Materials and Methods For this study 58 breast cancer patients (50 left; 8 right) treated in breath-hold were enrolled.

During CT simulation, each patient received a coaching session where three breath-holds of 25 seconds were acquired. Based on these breaths, the maximum breathing capacity was determined, and the breath-hold level was placed at 85% of the maximal breath-hold level. The breath-hold bandwidth was arbitrary chosen by the RTT managing the CT simulation. Daily CBCT was acquired each day of treatment, asking the patient to reproduce the breath hold level established during CT simulation. The body difference between simulation CT and CBCT image was measured, with the aim of quantifying inter-fraction variability present in each RT fraction after the couch shifts compensation. The absolute difference in terms of body between the first daily CBCT and planning CT was calculated along each beam axis. A treatment fraction was defined as “non-optimal” when the difference between CT image and CBCT was at least 4 millimetres in one of the beam axes. The ability of the bandwidth of predicting the probability of occurring a not-optimal fraction during treatment was investigated using the Wilcoxon Mann–Whitney test, setting a value of p<0.05 as significance level. A linear regression model was then calculated, and the predictive performance was evaluated in terms of area under Receiver Operating Characteristics (ROC) curve (AUC). Results A total of 24 patients out of 58 observed showed a variation greater than 4 millimetres at the first treatment fraction. This variation is significantly correlated with the bandwidth (p=0.034), resulting in a predictive model with an AUC of 0.65. The probability of observing a ‘not optimal fraction’ in function of the bandwidth are reported in table 1.

Conclusion Using data reported in table 1, it is possible to provide the RTT with a tool to minimize the risk of an adverse event during treatment, considering patient compliance and prescription dose.

PO-2272 Uptake of the Noona System for patient reported outcomes & education 1-year after implementation

S. Graham 1 , N. Akar 2 , A. Ward 3

1 Barking, Havering and Redbridge University Hospitals NHS Trust, Radiotherapy, Romford, United Kingdom; 2 Barking, Havering and Redbridge University Hospitals NHS Trust, Chemotherapy, Romford, United Kingdom; 3 Barking, Havering and Redbridge University Hospitals NHS Trust, Oncology, Romford, United Kingdom Purpose or Objective In August 2021 Barking, Havering, and Redbridge University Hospitals NHS Trust (BHRUT) were the first Trust in England to implement Varian’s Noona platform. This is a cloud hosted web/app-based platform designed to engage patients in their care with by providing an electronic platform to capture patient reported outcome measures (PROMs). Noona can provide streamlined clinical workflows and has integration capabilities with Varian’s ARIA electronic medical record system which used within BHRUTs radiotherapy and chemotherapy departments. By monitoring acute radiotherapy toxicity using PROMs we hope to be able to intervene earlier in management patients side effects, avoid A&E attendances and hospital admissions. Additionally, we aim to improve the efficiency of clinics, highlight patients who can be reviewed by non-clinician trained health care professionals, streamline the handover of patient cases between the teams in the oncology specialty and avoid patients having to travel large distances to the hospital unnecessarily. We were also the first site in Europe and the UK to use the patient education module. This has meant that all our patient information leaflets can be send to patients electronically including links to further information from the Macmillan website (including languages other than English). Given the digital nature of the platform, we wished to assess its uptake after 1 year to identify which patient groups were or were not engaging with the system. All patients are introduced to Noona once a referral for Radiotherapy or SACT has been received. Patients who opt-in to using Noona are sent an invite to access the system via an email or text message. Materials and Methods Using the analytics within Noona we analysed the number of patients who were using the app, their age range, cancer diagnosis, reason for use and satisfaction scores.

Results