Jonathan Sykes

Background: Diagnosis and management of cervical cancer require multiple investigations and specialist reviews, with delays leading to increased mortality and morbidity. Australia's Optimal Care Pathways (OCPs) recommends that patients with suspected diagnosis of cervical cancer should have their diagnostic processes completed within certain timeframes. Despite these guidelines, there is currently no benchmarking of cervical cancer timelines. Aims: This study aims to benchmark cervical cancer care and identify factors associated with delay. Materials and Methods: Patients who underwent radiotherapy for Stage I‐III cervical cancer between 2015 and 2024 at a major tertiary hospital in New South Wales (NSW) were included in the study. This hospital provides centralised gynae‐oncology care for all women in three NSW local health districts. Patients were stratified based on age, stage of disease, level of rurality and level of socioeconomic advantage/disadvantage. Each step of the diagnostic timeline was benchmarked against the OCPs. Univariate and multivariate logistic regression were performed to detect factors associated with delays. Results: The study cohort comprised 155 patients. Under 60% of women were managed in‐line with the OCPs recommended timelines. 86% ( n = 19/22) of women from the least disadvantaged socio‐economic tertile saw a gynae‐oncology specialist within the recommended timeframe compared to 47% (36/76) from the most disadvantaged socio‐economic tertile (OR: 6.24, CI: 1.60–24.27, p = 0.008). Conclusions: Recommended diagnostic timelines were not met in a significant proportion of women. Women from the greatest levels of socioeconomic disadvantage were more likely to face delays. Benchmarking processes need to be implemented in cancer care, to ensure equity in outcomes/processes for all patients.

Objective: Accurate dose delivery in the presence of anatomical motion and deformation remains a major challenge in radiation therapy. Real-time dose-guided radiation therapy addresses this challenge by integrating volumetric imaging, dose accumulation, and adaptive treatment in a quasi-continuous manner. Unlike previous studies limited to rigid motion or phantom data, this study employs a motion model to generate real-time volumetric images, enabling deformable dose-guided multileaf collimator (MLC) tracking in lung and liver cancer patients. Approach: Deformable dose-guided MLC tracking was simulated for ten patients (4 lung, 6 liver). The workflow included: (1) training a patient-specific linear regression-based motion model relating external respiratory signals (RPM) to deformation vector fields using planning 4DCT data, (2) generating volumetric images using treatment RPM signals, (3) simulating dose-guided MLC tracking comparing deformable, rigid and no tracking, and (4) evaluating the motion model accuracy using fiducial markers and kV imaging. Dosimetric accuracy was assessed using 3D Gamma analysis (2%/2 mm) and normalized root mean squared error by comparing simulated delivered dose to a motion-compensated baseline. Motion model error was quantified as the distance between predicted and ground truth marker positions. Main results: Deformable tracking significantly outperformed rigid and no tracking (p < 0.001), achieving a mean Gamma passing rate of 95.0 ± 6.0%, compared to 86.3 ± 10.0% and 74.8 ± 15.2%, respectively. Lung patients showed a greater benefit in Gamma passing rate (deformable: 98% vs. rigid: 85%, +13%) than liver patients (93% vs. 89%, +4%). The dosimetric benefit correlated with respiratory amplitude, with larger motion yielding greater improvements. The motion model demonstrated high accuracy, with a mean error of 0.1 ± 2.9 mm. Significance: This study demonstrates the feasibility and dosimetric advantage of deformable dose-guided MLC tracking using motion-model-derived volumetric imaging in patient data. These findings represent a critical step toward the clinical implementation of real-time dose-guided radiation therapy.

Introduction: Contouring organs at risk (OARs) manually in paediatric patients undergoing cranial‐spinal radiation therapy (CSI) is a time‐consuming, labour‐intensive task. This study aims to assess the accuracy and clinical acceptability of auto‐contours produced by the Siemens DirectORGANS auto‐contouring software on paediatric patients receiving CSI treatment. Methods: Auto‐contours of OARs were produced using the Siemens DirectORGANS Auto‐contouring Software from 20 paediatric CSI patients datasets that had previously been manually contoured by a paediatric specialist radiation therapist (RT) for plan production. Manual and auto‐contours were retrospectively analysed using quantitative (Dice Similarity Coefficient, Hausdorff Distance, Mean Distance to Agreement) and qualitative (Likert ratings, Turing test) assessment techniques. Results: Auto‐contoured structures were clinically acceptable for use without edits 72.8% of the time, and manual contours were clinically acceptable for use 91.7% of the time. The liver was the only auto‐contoured structure that performed better than the manual equivalent. Poor performance by the auto contouring tool was noted for structures surrounded by low contrast edges, such as the breasts, oesophagus, and brainstem, in both quantitative and qualitative assessment techniques. Brain auto‐contours were deemed not suitable for clinical use. Conclusion: The clinical acceptability of many of the auto‐contours favours the implementation of this auto‐contouring system for clinical use. However, prior to use, all contours should be critically assessed and edited accordingly. Our study results indicate that whilst auto‐contouring tools are designed for adult populations, they are suitable for use on paediatric patients when used with caution.

Introduction: Chemoradiotherapy with MRI‐guided brachytherapy boosts is the standard of care for locally advanced cervical cancer. Data from the RetroEMBRACE and EMBRACE I trials provide dose‐response curves for target volumes and OAR. This study evaluated plan quality, safety and toxicity following escalation of the CTV_HR D90 prescription from 80–90 Gy to 85–95 Gy for two different applicator designs.Methods: A retrospective dosimetric plan quality review was undertaken for consecutively treated locally advanced cervical cancer brachytherapy boosts between 2017 and 2022, and the relationships between implant total reference air kerma (TRAK), CTV_HR volume and vaginal dose were investigated. Safety and ≤grade 3 toxicity results were evaluated.Results: Seventy patients were identified. All patients received a CTV_HR D90 dose >85 Gy and rectum D2cm³ of 3 <90 Gy. Needles were used in 48.6% of patients, and the mean percentage needle TRAK was 38.2%. Increasing the percentage of needle TRAK decreased vaginal TRAK. Sixty‐three patients had more than 12 months of follow‐up (median 27 months). From this cohort, grade 3 or higher toxicity for vaginal, genitourinary, gastrointestinal and fistula events were seen in 3.2%, 1.6%, 0% and 3.2% of patients, respectively.Conclusion: Increased dose prescriptions and a change of applicator design were successfully adopted into an Australian practice. Applicator design and interstitial needle use affect the plan quality and ability to meet OAR dose constraints following target dose escalation. No safety concerns with needle use or dose escalation were identified.

Introduction: Safety and quality improvement are essential to clinical practice in radiation therapy as planning and treatment increase in complexity and sophistication. An incident learning system (ILS) is a safety and quality improvement tool that can aid risk mitigation to improve patient safety and quality of care. The aim of this study was to quantify the impact of implementing a new e‐ILS, Learning In Radiation ONcology (LIRON), on reporting and safety culture within a local health district (LHD).Methods: The ILS (LIRON) was implemented in 2020 with the intent of tracking actual incidents, near misses and procedural non‐compliances for analysis of root causes and contributing factors. A survey was conducted after 12 months of LIRON use, and distributed to radiation oncologists, radiation therapists and radiation oncology medical physicists within the LHD. Results were compared with the responses to a pre‐ILS implementation survey, to review changes in staff perceptions of safety culture, barriers to reporting and ILS understanding.Results: Survey response rates were similar at baseline and at the 12‐month follow‐up, 64% and 63%, respectively. Findings showed increased ILS participation (49–71%), increased perception of no barriers to reporting (34–43%) and increased encouragement to report (37–43%). Greater confidence in the department's ability to learn from the ILS was evident (24–46%).Conclusion: Initial findings of LIRON implementation show positive impact but warrant further long‐term review for greater understanding of its impact on staff perceptions, safety culture and improving departmental processes.

Introduction: Cranio‐spinal radiotherapy (CSI) is used to treat central nervous system malignancies in paediatric, adolescent/young adult (AYA), and adult patients. Its delivery in the paediatric/AYA population is particularly challenging across different age groups. This study aims to assess the setup variations and dosimetric impact of CSI in paediatric and AYA patients.Methods: This retrospective analysis included, 10 paediatric and AYA patients (aged 4–25) who underwent volumetric modulated arc therapy (VMAT) CSI between 2016 and 2022. Patient characteristics, diagnoses, prescribed CSI doses, and fractionation details were assessed. CT simulation and treatment planning followed standard protocols with setup errors were quantified by comparing daily treatment setup images with the planned position. The study evaluated the dosimetric impact on target volumes and organs at risk (OARs).Results: The setup errors were identified, ranging from 0.5 to 6.2 mm in different directions, especially in the cranio‐caudal direction. Despite these variations, there was minimal impact observed on the coverage of clinical target volumes (CTV) and doses to OARs (<1% relative change).Conclusion: Ensuring precise setup in paediatric and AYA patients undergoing CSI is essential to maintain adequate CTV coverage. Although occasional substantial setup variations occurred during treatment, they had a limited impact on CTV coverage and OAR doses when infrequent. Appropriate planning target volume (PTV) margins can effectively compensate for occasional shifts. However, systematic errors could compromise treatment quality if undetected. Regular off‐line review of patient set‐up trends is recommended.

Proton-to-photon comparative treatment planning is a current requirement of Australian Government funding for patients to receive proton beam therapy (PBT) overseas, and a future requirement for Medicare funding of PBT in Australia. Because of the fundamental differences in treatment plan creation and evaluation between PBT and conventional radiation therapy with x-rays (XRT), there is the potential for a lack of consistency in the process of comparing PBT and XRT treatment plans. This may have an impact on patient eligibility assessment for PBT. The objective of these guidelines is to provide a practical reference document for centres performing proton-to-photon comparative planning and thereby facilitate national uniformity.

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.