Maximise your continuing professional development (CPD) by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS—Volume 72, Issue 4, December 2025.
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This letter is in response to https://doi.org/10.1002/jmrs.856, Impact of Pre-Examination Video Education in Gd-EOB-DTPA-Enhanced Liver MRI: Correspondence.
The use of medical imaging services has increased globally with a concurrent increase in radiology, nuclear medicine and medical imaging (RNMI) research. However, New Zealand's RNMI research output relative to global trends is under-examined. This project evaluates New Zealand's RNMI research output between 1996 and 2022 compared to selected countries while highlighting global RNMI research output trends.
A bibliometric-based performance analysis was conducted using publication data from the SCImago Journal, the Country Rank portal, Clarivate InCites Benchmarking, and the Analytics platform. Registration data of RNMI professionals by country was collected to evaluate the relationship between research output and the number of registered professionals.
Among the seven selected countries (the United States, United Kingdom, Canada, Australia, Ireland, New Zealand and South Africa), New Zealand's research output was low, even when adjusted for population size and the number of professionals. A significant positive correlation was found between the number of registered RNMI professionals and the number of RNMI publications. Despite this, New Zealand had the highest percentage of RNMI documents cited.
Although New Zealand's RNMI publications follow the global upward trend, it does so at a proportionate loss. New Zealand ranked low in most bibliometric indicators apart from the percentage of documents cited, where it showed a notable citation impact. Emphasising research, increasing collaborative efforts, and undertaking further statistical analyses may enhance New Zealand's RNMI research output.
There has been an uptake in hypofractionation radiotherapy schedules (> 2.45 Gy per fraction) worldwide over the last decade. The aim of this paper was to evaluate the change in fractionation schedules for patients undergoing radiotherapy in regional Queensland. The influence of treatment site, intent and patient social circumstances was assessed, identifying any current gaps in practice.
This retrospective clinical audit, included patients who underwent radiotherapy in 2012, 2019 and 2022 at a large regional department. This allowed a 10-year analysis and an evaluation of any impact of COVID-19. Demographic data and treatment information was collected and analysed using descriptive statistics.
There was a notable trend favouring hypofractionation for patients treated for breast and prostate cancer. In 2012, 62.7% of breast cancer patients were treated with conventional fractionation and 37.3% were treated with hypofractionation, versus 2.4% and 92.1%, respectively, in 2022. Prostate cancer fractionation changed from 99.4% of patients treated with conventional fractionation and 0.6% with hypofractionation in 2012 to 23.2% and 74.1%, respectively, in 2022. The standard of care also shifted for palliative intent, with lung, brain and bone metastases in 2022 being treated with increased hypofractionated and ultra-hypofractionated radiotherapy (> 5 Gy per fraction). This coincides with more complex and modulated treatments being readily available, such as stereotactic radiotherapy and volumetric modulated arc therapy. Hypofractionated treatments, however, were not influenced by the social factors of patients, having no distinct relationship with Indigenous status, age and patients' distance to treatment.
This study has validated the increase in hypofractionated treatments over a range of cancer sites and treatment intents, with increased treatment complexity. This has a direct impact on both departmental resources and patient-centred care, offering value-based radiotherapy.
Introduction: Paediatric diagnostic reference levels (DRLs) are dose levels for typical medical imaging examinations for broadly defined types of equipment with weight-stratification preferred by the International Commission on Radiological Protection. Australia has never published paediatric DRLs for general radiography. The aim of this study was to collect radiation dose metrics for commonly performed radiographic projections in children in Australia and propose weight-based DRLs.
Methods: Ethics approval was granted to collect data, along with a waiver of consent. Radiographs were acquired in accordance with local protocols using direct digital X-ray equipment for children who presented for routine radiographic imaging. A spreadsheet was provided to each centre to record the patient's age and weight, as well as tube voltage and current-time product, source-to-image distance, use of a grid, additional filtration, automatic exposure control chamber selection and the displayed air kerma area product (KAP). Facility reference levels (FRLs) were calculated as the median for each X-ray unit based on data submitted for a minimum of three patients. The 75th percentiles of the FRLs across nine X-ray units from five centres were calculated as the proposed Local DRLs (LDRLs).
Results: The most commonly radiographed body parts in children were the chest, wrist, abdomen, elbow and foot. The proposed LDRLs range from 4 mGy•cm2 (oblique hand in 5-15 kg) to 884 mGy•cm2 (antero-posterior pelvis in 50-80 kg).
Conclusion: The estimation of LDRLs for radiographs from a weight-based patient study offers Australian reference values for guidance in the optimisation process.
Maximise your continuing professional development (CPD) by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS—Volume 72, Issue 4, December 2025.
Scan this QR code to find the answers.
Virtual reality (VR) has been increasingly recognised as a beneficial pedagogical tool in radiography education, particularly for skills training. This pilot study aims to gain insight into the viability of VR as a pedagogical instrument in a radiographic technique course within a Norwegian bachelor's programme in radiography by assessing users' experiences.
A cross-sectional study was conducted involving all first-year radiography students from a single bachelor programme in Norway. The study included a preliminary survey to gauge students' expectations prior to their first VR session and a main survey following the completion of the course. The surveys assessed demographics, prior VR experience, experiences with the use of VR as a learning tool and possible improvements. VR training was facilitated using Skilitics radiography simulation software across six stations equipped with Oculus Rift VR gear.
Results indicated a significant difference between students' expectations and their actual experiences with VR in skills learning. While initial expectations were high, only 37% of students were content with VR training. Major issues highlighted included technical problems and limited pre-session training. Students expressed a preference for more VR stations, teacher guidance and better software features.
Although VR holds potential as a supplementary tool in radiography education, the study identified several areas for improvement in the pedagogical approach. Pre-session training, teacher assistance during the training sessions and feedback after the session are recommended to maximise the educational benefits of VR in radiography skills training.
Image guided-radiation therapy (IGRT) protocols are adopted to ensure the accurate dose delivery of patient treatments. This is especially important in hypofractionated treatments, such as stereotactic body radiation therapy (SBRT), as high doses of radiation are delivered, and incorrect treatment can have a significant impact on tumour control and toxicity. This study aimed to establish mean treatment times from the localisation image to the post-treatment image in SBRT liver, lung and spine patients that utilised Elekta Intrafraction Imaging (IFI). The magnitude of intrafraction motion exhibited as time elapses during the treatment fraction was also determined.
IGRT data for 20 SBRT patients was retrospectively collected, including imaging times and shifts made from each pre-, during and post-treatment cone-beam computed tomography (CBCT) scan. Total treatment fraction time, time between each image acquired and the 3D vector of the shifts were calculated. Descriptive statistical analysis was performed.
The IGRT data associated with 332 CBCT images was evaluated. The average treatment time was longest in the liver (19.3 min), followed by lung (14.9 min) and spine (14.2 min). Liver patients had a mean shift 3D vector (0.1 cm), with 7.8% of shifts > 0.3 cm. Lung patients had a mean vector of 0.1 cm with 3.8% > 0.3 cm, and spine patients had a mean vector of 0 cm with 0% > 0.2 cm. Vectors > 0.3 cm occurred at multiple imaging timepoints (range: 4.9–24.4 min) for liver and lung patients.
Intrafraction imaging is required in liver and lung SBRT treatments to identify instances where clinical tolerances are exceeded.
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring in 30%–55% of patients within 5 years after surgery. This review analysed existing studies on the utilisation of artificial intelligence (AI), incorporating CT, PET, and clinical data, for predicting recurrence risk in early-stage NSCLCs.
A literature search was conducted across multiple databases, focusing on studies published between 2018 and 2024 that employed radiomics, machine learning, and deep learning based on preoperative positron emission tomography (PET), computed tomography (CT), and PET/CT, with or without clinical data integration. Sixteen studies met the inclusion criteria and were assessed for methodological quality using the METhodological RadiomICs Score (METRICS).
The reviewed studies demonstrated the potential of radiomics and AI models in predicting postoperative recurrence risk. Various approaches showed promising results, including handcrafted radiomics features, deep learning models, and multimodal models combining different imaging modalities with clinical data. However, several challenges and limitations were identified, such as small sample sizes, lack of external validation, interpretability issues, and the need for effective multimodal imaging techniques.
Future research should focus on conducting larger, prospective, multicentre studies, improving data integration and interpretability, enhancing the fusion of imaging modalities, assessing clinical utility, standardising methodologies, and fostering collaboration among researchers and institutions. Addressing these aspects will advance the development of robust and generalizable AI models for predicting postsurgical recurrence risk in early-stage NSCLC, ultimately improving patient care and outcomes.
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