Background and purpose:Adaptive Four-Dimensional Cone-Beam Computed Tomography (4DCBCT) can reduce scan time and imaging dose in radiotherapy. This is achieved by modulating the projection acquisition rate and gantry rotation speed in response to real-time changes in patient breathing, together with motion-compensated image reconstruction. This study aimed to evaluate the clinical image quality of a fast adaptive 4DCBCT acquisition compared with conventional 4DCBCT in the treatment of lung cancer. Materials and methods:Image datasets from the Adaptive 4DCBCT (ADAPT) clinical trial (ACTRN12618001440213), which included 30 patients treated for lung cancer, were analyzed. Two scan types were assessed: fast adaptive 4DCBCT (200 projections acquired over 20 breathing cycles, approximately 60-80s) and conventional 4DCBCT (1320 projections acquired over approximately 80 breathing cycles, 4 min). Two radiation oncologists and four radiation therapists, blinded to the image acquisition technique, independently rated the clinical utility of each scan using a two-question survey. Tumor visibility was rated on a three-point scale, and overall image quality was rated on a ten-point scale. A paired t-test was used to compare scores across acquisition techniques. Results:Fast adaptive 4DCBCT showed a mean tumor visibility score of 2.3 ± 0.8, compared to conventional 4DCBCT (2.4 ± 0.7). For general image quality, fast adaptive 4DCBCT achieved a mean score of 4.1 ± 1.3, compared to conventional 4DCBCT, which had a mean score of 4.3 ± 1.2. There were no statistically significant differences in tumor visibility or image quality scores between fast adaptive 4DCBCT and conventional 4DCBCT scans. Conclusion:Fast adaptive 4DCBCT achieved similar image quality scores to conventional 4DCBCT while requiring only 15% of the imaging dose and 25% of the scan time. This study confirms the clinical feasibility of adaptive scanning protocols for use in radiation therapy for lung cancer.

Standardized nomenclature for radiotherapy (RT) organs-at-risk (OARs) and target volumes (TVs) is essential for using large retrospective imaging and treatment-planning datasets. However, clinical structure names vary substantially across centres, making manual standardization time-consuming and resource-intensive. This study aims to develop a transformer-based multimodal model to automatically standardize the breast RT structure names into protocol-standard labels by combining raw clinical-use text, image features, transformer-derived spatial context, and geometric features. The model was trained and internally validated on a curated dataset from a single centre comprising 1436 breast cancer RT patients, and externally tested on 463 patients from five independent centres. During internal validation, the full multimodal model achieved a weighted F1-score of 98.08 +/- 0.09%. Across the five external centres, the full model achieved weighted F1-scores ranging from 87.75% to 95.40%, with OAR accuracy reaching 100% in most centres and 96% in one centre. For primary and nodal TVs, external accuracy ranged from 80% to 97%. With reduced training data, the transformer-based model outperformed the CNN baseline when trained using 50%-80% of the available training data, with the largest difference observed at 50% training data (88.12% vs 75.36% weighted F1). These results support transformer-based multimodal learning as a scalable approach for retrospective RT nomenclature standardization, with potential application in clinical quality assurance workflows, offering a substantial reduction in manual effort and time.

Uptake of lung cancer screening (LCS) in high-risk populations remains suboptimal internationally. Primary care practitioners play a critical role in identifying eligible patients and initiating referrals for LCS. Targeted implementation strategies are needed to address health care barriers to the uptake of LCS such as limited awareness, eligibility assessment, low engagement, and poor health system preparedness. Implementation trials are needed to determine optimal decision-making and participant knowledge gains to ultimately increase screening uptake. The Ready to Screen trial is a cluster randomized controlled implementation trial to compare participants’ intention to screen for lung cancer in the Australian National Lung Cancer Screening Program (hereafter ‘the Program’) between bundled (intervention: core + link to multilingual trial website + SMS and email reminders, clinical decision support prompts for general practitioners) and core (control (core): initial letter mail out with LCS brochure only) implementation strategies. Twenty-eight general practices recruited across Australia will be randomly allocated (1:1 ratio) to either control or intervention. Practices will generate eligible patient lists using medical records to issue participation invitations. Eligible patients are aged 50–70 years and currently smoke or have quit within the past 10 years or have an unknown quit date. The primary outcome is participant intention to screen from self-report survey at patient recruitment. Secondary outcomes will be evaluated using the RE-AIM framework—examining Reach, Effectiveness (including cost-effectiveness), Adoption, Implementation and Maintenance. The PRISM framework will guide assessment of multi-level contextual factors hypothesized to influence these outcomes. Data collection will include trial recruitment and practice records, participant and provider self-report surveys, and semi-structured interviews. This trial will generate timely evidence about the effectiveness and cost-effectiveness of the bundled implementation strategy to support delivery of the Program within primary care practices. Findings will provide insights into contextual factors shaping implementation success and inform the future scaling and sustainability of LCS in Australia and internationally. ACTRN12625000045415 registered on 20/01/2025.

Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical landmarks, making upfront training data preparation difficult for an automated contour QA segmentation model. We investigate anatomical priors, derived from surrounding organ segmentations, to provide spatial context and improve TOPGEAR CTV segmentation accuracy. We also evaluate active learning, iteratively expanding the training dataset by selecting cases expected to improve performance. One hundred TOPGEAR CT scans were retrospectively analyzed. An initial set of 10 expert-contoured cases was used to train an nnU-Net model. TotalSegmentator generated a voxel-wise anatomical prior map from surrounding structures as an additional input channel. Active learning was simulated over four iterations, selecting cases by model uncertainty and segmentation performance. All models used five-fold cross-validation for an ensemble uncertainty measure. Evaluation used a hold-out testing set of 50 cases. The anatomical prior improved CTV segmentation accuracy, increasing mean Dice Similarity Coefficient (DSC) from 0.84 to 0.86. Active learning similarly improved performance to 0.86, with greatest benefit in the final round. Combining the anatomical prior with active learning achieved the highest accuracy, with a DSC of 0.87. Model uncertainty correlated with DSC, supporting its use in identifying suboptimal predictions and guiding active learning. Anatomical priors and active learning each improved CTV segmentation accuracy and generalizability, with their combination achieving the best performance, supporting integration into segmentation model development for automated contour QA in radiotherapy clinical trials.

Sex and gender influence cancer incidence, treatment response, and outcomes, yet reporting of these variables remains inconsistent in clinical research. Australia represents a distinct research governance context, where sex and gender considerations are recommended but not systematically embedded in regulatory oversight, and where no national evaluations of reporting in cancer trials exist. By examining Australian cancer randomised controlled trials (RCTs) published from 2014-2024, this study addresses a critical evidence gap and extends prior international assessments by providing the first national-level analysis of reporting practices in relation to the Sex and Gender Equity in Research (SAGER) guidelines. A review of RCTs conducted in Australia or led by Australian investigators and published between January 2014 and October 2024 was undertaken. Studies were assessed for adherence to key SAGER criteria, including correct use of terminology, reporting of sex/gender in abstracts and tables, consideration in study design, inclusion of sex/gender-based analyses, and discussion of implications. For temporal comparisons, the introduction of the SAGER guidelines in 2016 was used as the cut‑off, with trials grouped as pre‑SAGER (2014–2016) and post‑SAGER (2017–2024). Of 128 eligible studies, none reported how sex or gender was defined. Overall, 50

Research into unmet needs in older populations with cancer is crucial, as existing studies often overlook these groups and fail to capture their unique experiences. Addressing these gaps is essential to improve quality of life and reduce psychological distress, particularly in those diagnosed with lung cancer, who face a higher burden of unmet needs than other cancer diagnoses. AIM:To explore the needs of older people with lung cancer and their perceptions and experiences of healthcare, social support and daily living in one of Australia's most culturally diverse communities. METHODS:A descriptive qualitative study was conducted from January 2023 to May 2024. Eligible participants were aged 70+ with lung cancer. Data collection occurred through face-to-face interviews, which were audio recorded and transcribed. Recruitment concluded after no new themes were identified. Data were analysed thematically and reported using the COREQ checklist. RESULTS:Seventeen participants (mean age, 80.4), including 10 from culturally and linguistically diverse backgrounds, were interviewed. Four themes, including seven subthemes, were identified. Main themes included: (i) the complexity of ageing, frailty and chronicity in cancer care; (ii) navigating uncertainty: emotional response and adaptive coping; (iii) family as a pillar of functional and emotional support in cancer care and (iv) how health literacy needs shape decision-making and engagement with health services. CONCLUSION:Older people with lung cancer face multiple unmet needs shaped by ageing, multimorbidity, poor health literacy and psychological distress. While geriatric screening and assessment can identify unmet needs, addressing these requires personalised supportive care interventions.

AIM:Smoking is a chronic relapsing condition that is under-reported in oncology settings. People who report current smoking (CS) and those who report recently quitting smoking (RQ) should receive cessation support when they are diagnosed with cancer. The study aimed to identify whether differences exist in the smoking cessation support given to CS and RQ in oncology and what advice is given regarding the benefits of cessation. METHOD:A survey exploring smoking cessation practices was completed by oncology clinicians (medical, nursing, and allied health) at nine cancer centers in Australia. Data were analyzed using mixed-effects ordinal regression modeling. RESULTS:Across the 177 clinicians completing the survey, the reported provision of smoking cessation care was significantly higher for CS than for RQ in relation to asking about smoking status (odds ratio [OR] 3.03, p = 0.001), advice on the benefits of quitting (OR 2.86, p = 0.001), and advice to call the Quitline (OR 5.08, p < 0.001). Exploratory analyses indicated doctors and nurse specialists were four times more likely to report referring CS to a Quitline compared to RQ (OR 4.38, p = 0.001; OR 4.29, 95%, p = 0.005, respectively). The cessation benefits that clinicians most often cited to their patients was that quitting "can reduce the chance of developing treatment complications and side effects". CONCLUSION:The relative lack of smoking cessation care provided to RQ in oncology suggests that the high risk of smoking relapse is not well-recognized. Greater awareness and training are needed regarding advising RQ about the survival-specific benefits of continuing to not smoke, offering referrals, and offering follow-up support.

MET exon 14 skipping mutation (METex14) is a key oncogenic driver in 2% of non-small cell lung carcinoma (NSCLC) and is enriched in sarcomatoid carcinoma (SAC). However, the prevalence and significance of METex14 NSCLC in the Australian population is not known. We evaluated the incidence, clinical, molecular, and histopathological features of METex14 and SAC cases in an Australian tertiary referral centre. We retrospectively analysed clinical, molecular, histopathological, and immunohistochemical data of NSCLC cases undergoing DNA and/or RNA fusion panel next-generation sequencing (NGS) between 1 July 2021 and 15 May 2024. Among 1267 NSCLC, 880 (69%) cases had DNA NGS only, 359 (28%) cases had both DNA NGS and RNA fusion panel, and 28 (2%) cases had RNA fusion panel only. Overall, 29 (2.3%) cases had METex14, 14 (1.1%) had MET amplification, and 10 (0.8%) had MET R988C. Of the 29 METex14 patients, 15 (52%) were women and 14 (48%) were men. METex14 patients were older than those who were MET wild-type, or with EGFR or KRAS mutations (median age 76 vs 71, 69, and 71, respectively) and were less likely to be smokers than KRAS-mutated cases (58% vs 92%, p<0.0001). Most METex14 cases were adenocarcinomas (76%), with 14% classified as SAC. Programmed death-ligand 1 (PD-L1) expression was higher in METex14 than in EGFR-mutated cases. Median survival for METex14 patients was 26 months (stage I), not reached (stage II), 8 months (stage III), and 3.5 months (stage IV). Among 28 SAC cases, 57% harboured oncogenic mutations, including KRAS (18%), METex14 (14%), BRAF V600E (7%), and EGFR exon 19 deletion (4%). SAC exhibited significantly higher PD-L1 expression (mean tumour proportion score 71 vs 30, p<0.0001) and a greater proportion of high PD-L1 expressors (82% vs 30%, p<0.0001) than other NSCLC subtypes. Stage IV SAC patients had a median survival of only 2 months. In summary, in our cohort of NSCLC, METex14 mutation was found in 2% of cases using DNA NGS alone ​and up to 3% when both DNA NGS and RNA fusion panel testing were employed. METex14 mutations were more common in elderly patients, with an equal gender distribution and a high proportion of non-smokers. While most cases were adenocarcinomas, SAC was enriched for METex14. SAC was an aggressive NSCLC subtype, with KRAS and METex14 as the most common driver mutations. Given the high prevalence of PD-L1 expression in SAC, further research on immunotherapy efficacy in this group is warranted.

BACKGROUND AND PURPOSE:Automated contour quality assurance (QA) has the potential to reduce resource and cost requirements for clinical trial QA. While several studies have developed such models, few have been translated for use in prospective real-world trials. This study aimed to implement a contour QA tool using a previously developed model and evaluate its performance during deployment within the TROG 18.01 NINJA prostate cancer trial. MATERIALS AND METHODS:A software tool was developed using an existing prostate clinical target volume (CTV) QA model and integrated into the trial QA workflow. A pilot study was conducted over an 18-month period, during which 56 CTVs were assessed. Reports generated by the tool flagged cases for review and were provided to radiation oncologists to support QA processes. RESULTS:All five protocol-violating CTVs were correctly identified during deployment, yielding a sensitivity of 1.0. However, a higher-than-expected false positive rate resulted in an accuracy of 0.46 and specificity of 0.41. Retrospective analysis showed that many cases submitted deviated from the model's training distribution, primarily due to inconsistencies in MRI acquisition and variation in submitted CTV definitions. Incorporating out-of-distribution detection based on histogram correlation and model uncertainty improved accuracy to 0.69 for in-distribution cases. Radiation oncologists reported time savings of up to 60 min per case. However, preparation of data remained time intensive for QA coordinators, highlighting the need for further workflow automation. CONCLUSION:These findings support the feasibility of automated contour QA in multicentre trials and offer guidance for future implementation at scale.

PURPOSE:Patients who smoke tobacco during and after a cancer diagnosis have poorer health outcomes. Oncology healthcare providers (HCPs) are crucial to providing smoking cessation support. The study examined the characteristics associated with differences in HCPs' smoking cessation practices. METHODS:As part of the Care to Quit trial, a cross-sectional survey exploring smoking cessation practices was completed by HCPs across nine cancer centers in New South Wales and Victoria, Australia. RESULTS:One hundred and seventy-seven HCPs completed the survey. Over half of the HCP respondents reported asking patients their smoking status, but fewer than half advised patients about the benefits of quitting, referred patients to behavioral support such as Quitline, or offered pharmacotherapy medication. All components of the "3A's" model (Ask, Advise, Act) were more likely to be completed by doctors compared to registered nurses (OR: 7.86, 95% CI: 3.64, 16.95, p<0.001), by those with more years of practice (OR: 0.26, 95% CI: 0.07-0.93, p = 0.039), and those who had received smoking cessation training (OR: 3.91, 95% CI: 1.80, 8.48, p = 0.001). Multivariate analyses also identified differences in the amount of cancer-specific advice provided between occupation type (p<0.001) and years of practice (p = 0.021). CONCLUSION:The need for smoking cessation care training in oncology continues to be apparent. Training in prescribing pharmacotherapies (for doctors) or supporting the use of pharmacotherapies (for nurses) is a particular "gap." Differences between the roles and engagement of doctors and nurses in relation to smoking cessation care should be carefully considered when developing site-specific models of cessation care and providing training.

INTRODUCTION:Optimal radiotherapy (RT) use in cancer patients results in substantial 5-year local control (LC) and overall survival (OS) benefits at the population level. This study aimed to estimate the average per capita cost of the first course of RT treatment and the associated cost per LC and OS outcomes, both overall and by cancer stage. METHODS:Data on RT activities from 2017 to 2020 for lung, rectum, cervix, prostate, brain and head and neck (H&N) cancers were extracted from South-Western Sydney Local Health District electronic oncology information system MOSAIQ (Elekta, version 2.63). Costs were assigned based on activity codes and adjusted for yearly inflation rates. The average cost per treatment course was calculated (average cost per activity × number of fractions). Costs per 5- and 1-year LC and OS outcomes were estimated for all stages and for stages I-II and III. RESULTS:A total of 106,174 RT activities were extracted. The average cost of an RT treatment course was highest for prostate cancer ($10,332) and lowest for lung cancer ($5598). The lowest costs per 5-year outcome were observed for cervical cancers (LC: $15,780, OS: $28,370) and H&N cancers (LC: $17,500, OS: $29,750). The cost per 5-year LC and OS outcome remained below $100,000 across all stages for each cancer type, except for prostate cancer, where the cost per OS outcome exceeded this level. CONCLUSION:This study demonstrates that the absolute costs associated with achieving 5-year local control and overall survival outcomes with radiotherapy are comparatively low across several major cancer types. These findings highlight the efficiency of radiotherapy in delivering meaningful clinical outcomes and can help inform service planning, investment decisions and prioritisation of radiotherapy within cancer care strategies.

Shortening treatment time with moderately hypofractionated radiotherapy benefits patients by reducing inconvenience and costs, but its use in the definitive treatment of unresectable Stage 3 non-small cell lung cancer is controversial due to lack of level one evidence and toxicity concerns. Pivotal systemic therapy trials utilize conventionally fractionated chemoradiation at 2 Gy per fraction given over 6 weeks. In practice, 4 weeks of chemoradiation at 2.75 Gy per fraction is sometimes employed to reduce the treatment burden for selected patients, especially those who are older or have comorbidities. It is uncertain if the two fractionation regimens are similar in biological effectiveness, especially with varying systemic therapy. This systematic review aimed to collate the survival and toxicity outcomes for 4 weeks of moderately hypofractionated chemoradiation, using > 50 -60 Gy in 20 fractions. Eight studies met the eligibility criteria; seven studies were from a database search of MEDLINE, EMBASE, Cochrane Library, and Web of Science and one study was added later. Two studies were prospective randomized trials and six were retrospective cohort studies. No study included immunotherapy. The historical evidence has been limited, but emerging data is promising, especially when compared to outcomes of standard chemoradiation. Thus, further investigation of this strategy is justified.

BACKGROUND:Current studies assessing the reproducibility of radiomic features from gynaecological magnetic resonance images (MRIs) with interobserver contour variation (IOV) have been limited to ≤3 observers. This number of observers is insufficient to demonstrate the full range of IOV. PURPOSE:To assess the impact of observer numbers when investigating the reproducibility of gynaecological T2W-MRI radiomic features with IOV. METHODS:20 gynaecological cancer T2W-MRIs had the gross tumor volume (GTV), bladder, rectum, uterus, parametrium, and vagina delineated by 6 observers to create a 2-, 3-, 4-, 5-, and 6-observer dataset for each patient. IOV was assessed for each observer dataset and structure using the dice similarity coefficient, mean surface distance, and mean volume overlap variance. 107 radiomic features were extracted from each observer contour using PyRadiomics. The reproducibility of each radiomic feature was assessed for each observer dataset and structure using an intraclass correlation coefficient (ICC). An ICC estimate greater than 0.75 or 0.90 was classified as having good or excellent reproducibility, respectively. RESULTS:The GTV had a decrease in the number of features with good/excellent reproducibility when the number of observers in the dataset increased. Volumes with less IOV, such as the bladder and uterus, did not show this same trend, with consistent numbers of features with good/excellent reproducibility across all observer datasets. CONCLUSION:Determining the reproducibility of gynaecological T2W-MRI radiomic features to IOV with three or fewer observers is not adequate to display the full impact of IOV for GTVs.

Background and Objective: Approximately 16,000 new cases of lung cancer are diagnosed each year in Australia and Aotearoa New Zealand, and it is the leading cause of cancer death in the region. Unwarranted variation in lung cancer care and outcomes has been described for many years, although clinical quality indicators to facilitate benchmarking across Australasia have not been established. The purpose of this study was to establish clinical quality indicators applicable to lung and other thoracic cancers across Australia and Aotearoa New Zealand. Methods: Following a literature review, a modified three round eDelphi consensus process was completed between October 2022 and June 2023. Participants included clinicians from all relevant disciplines, patient advocates, researchers and other stakeholders, with representatives from all Australian states and territories and Aotearoa New Zealand. Consensus was set at a threshold of 70%, with the first two rounds conducted as online surveys, and the final round held as a hybrid in person and virtual consensus meeting. Results: The literature review identified 422 international thoracic oncology indicators, and a total of 71 indicators were evaluated over the course of the Delphi consensus. Ultimately, 27 clinical quality indicators reached consensus, covering the continuum of thoracic oncologic care from diagnosis to first line treatment. Indicators benchmarking supportive care were poorly represented. Attendant numeric quality standards were developed to facilitate benchmarking. Conclusion: Twenty-seven clinical quality indicators relevant to thoracic oncology care in Australasia were developed. Real world implementation will now be explored utilizing a prospective dataset collected across Australia.