Measurement that matters: A systematic review and modified Delphi of multidisciplinary colorectal cancer quality indicators.

AIM: To develop a priority set of quality indicators (QIs) for use by colorectal cancer (CRC) multidisciplinary teams (MDTs). METHODS: The review search strategy was executed in four databases from 2009-August 2019. Two reviewers screened abstracts/manuscripts. Candidate QIs and characteristics were extracted using a tailored abstraction tool and assessed for scientific soundness. To prioritize candidate indicators, a modified Delphi consensus process was conducted. Consensus was sought over two rounds; (1) multidisciplinary expert workshops to identify relevance to Australian CRC MDTs, and (2) an online survey to prioritize QIs by clinical importance. RESULTS: A total of 93 unique QIs were extracted from 118 studies and categorized into domains of care within the CRC patient pathway. Approximately half the QIs involved more than one discipline (52.7%). One-third of QIs related to surgery of primary CRC (31.2%). QIs on supportive care (6%) and neoadjuvant therapy (6%) were limited. In the Delphi Round 1, workshop participants (n = 12) assessed 93 QIs and produced consensus on retaining 49 QIs including six new QIs. In Round 2, survey participants (n = 44) rated QIs and prioritized a final 26 QIs across all domains of care and disciplines with a concordance level > 80%. Participants represented all MDT disciplines, predominantly surgical (32%), radiation (23%) and medical (20%) oncology, and nursing (18%), across six Australian states, with an even spread of experience level. CONCLUSION: This study identified a large number of existing CRC QIs and prioritized the most clinically relevant QIs for use by Australian MDTs to measure and monitor their performance.

Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low-dose 4D cone beam CT imaging.

BACKGROUND: The adoption of four-dimensional cone beam computed tomography (4DCBCT) for image-guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240 s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2 s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality. PURPOSE: This study investigates the impact of gantry velocity and angular separation between x-ray projections on image quality and its implication for fast low-dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x-ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state-of-the-art reconstruction methods. METHODS: This study considers fast low-dose 4DCBCT acquisitions (60-80 s, 200-projection scans). To assess the impact of adaptive gantry rotations, the angular position of x-ray projections from adaptive 4DCBCT acquisitions from a 30-patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2 s, 60 s, 120 s, 240 s) were simulated by sampling x-ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac-Torso (XCAT) digital phantom was used to simulate projections to remove patient-specific image quality variables. Image reconstruction was performed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), and Motion-Compensated-MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity-Index-Measure (SSIM), Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR), Tissue-Interface-Width-Diaphragm (TIW-D), and Tissue-Interface-Width-Tumor (TIW-T). RESULTS: Patient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB-reconstructions, average patient angular gaps produced SSIM-0.98, CNR-13.6, SNR-34.8, TIW-D-1.5 mm, and TIW-T-2.0 mm, static angular gap 40° produced SSIM-0.92, CNR-6.8, SNR-6.7, TIW-D-5.7 mm, and TIW-T-5.9 mm and ideal produced SSIM-1.00, CNR-13.6, SNR-34.8, TIW-D-1.5 mm, and TIW-T-2.0 mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts. CONCLUSION: Very fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion-compensated reconstruction is performed. Importantly, the angular separation between x-ray projections within each individual respiratory bin had minimal effect on the image quality of fast low-dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.

Central airway squamous metaplasia following radiation therapy mimicking local tumour recurrence.

Radiation therapy can result in injury to the lung parenchyma and central airways; the latter is less well documented in the literature. Here, we describe a 65-year-old Caucasian male, who developed focal endobronchial nodules and right main bronchial stenosis suggesting tumour recurrence, 32 months following curative intent concurrent chemoradiation therapy for Stage 3B squamous cell carcinoma of the lung. Computed tomography and positron emission tomography results are detailed. Flexible bronchoscopy with bronchial biopsies revealed squamous metaplasia rather than malignant tumour recurrence, with ongoing observation planned.

Central airway squamous metaplasia following radiation therapy mimicking local tumour recurrence.

Radiation therapy can result in injury to the lung parenchyma and central airways; the latter is less well documented in the literature. Here, we describe a 65-year-old Caucasian male, who developed focal endobronchial nodules and right main bronchial stenosis suggesting tumour recurrence, 32 months following curative intent concurrent chemoradiation therapy for Stage 3B squamous cell carcinoma of the lung. Computed tomography and positron emission tomography results are detailed. Flexible bronchoscopy with bronchial biopsies revealed squamous metaplasia rather than malignant tumour recurrence, with ongoing observation planned.

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation.

BACKGROUND AND PURPOSE: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. MATERIALS AND METHODS: The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. RESULTS: The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. CONCLUSION: To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

SABR for Early Non-Small Cell Lung Cancer: Changes in Pulmonary Function, Dyspnea, and Quality of Life.

PURPOSE: The aim of this study was to report pulmonary function tests (PFTs) and clinician-reported and patient-reported quality-of-life (QoL) outcomes on a cohort of patients with non-small cell lung cancer (NSCLC) treated with SABR. METHODS AND MATERIALS: A total of 119 patients with NSCLC were treated with SABR in the prospective cohort SSBROC study of patients with T1-T2N0M0 NSCLC. PFTs and QoL measures were obtained at baseline pretreatment and at 6-month intervals. Here we report on the 6- to 18-month time points. Analysis of covariance (ANCOVA) methods adjusting for baseline analyzed potential predictors on outcomes of PFTs and patient-reported dyspnea at 18 months. RESULTS: The only statistically significant decline in PFTs was seen in forced expiratory volume in 1 second (FEV1) at 18 months post-SABR, with a decline of -0.11 L (P = .0087; 95% CI, -0.18 to -0.02). Of potential predictors of decline, only a 1-unit increase in smoking pack-years resulted in a -0.12 change in diffusing capacity for carbon monoxide (P = .026; 95% CI, -0.02 to -0.23) and a 0.003 decrease in FEV1 (P = .026; 95% CI, -0.006 to -0.0004). For patient-reported outcomes, statistically significant worsening in both the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (QLQ-C30 Version 3) and the lung module (QLQ-LC13) dyspnea scores occurred at the 18-month time point, but not earlier. No potential predictors of worsening dyspnea were statistically significant. There was no statistically significant decline in clinician-reported outcomes or global QoL scores. CONCLUSIONS: We found a statistically significant decline in FEV1 at 18 months posttreatment. Smoking pack-years was a predictor for decline in diffusing capacity for carbon monoxide and FEV1 at 18 months. Worsening of patient-reported dyspnea scores was observed, consistent with the expected progression of lung comorbid disease.

Qualitative study of international key informants' perspectives on the current and future state of healthcare quality measurement and feedback.

OBJECTIVES: The aim of this study is to explore the current and future state of quality measurement and feedback and identify factors influencing measurement feedback systems, including the barriers and enablers to their effective design, implementation, use and translation into quality improvement. DESIGN: This qualitative study used semistructured interviews with key informants. A deductive framework analysis was conducted to code transcripts to the Theoretical Domains Framework (TDF). An inductive analysis was used to produce subthemes and belief statements within each TDF domain. SETTING: All interviews were conducted by videoconference and audio-recorded. PARTICIPANTS: Key informants were purposively sampled experts in quality measurement and feedback, including clinical (n=5), government (n=5), research (n=4) and health service leaders (n=3) from Australia (n=7), the USA (n=4), the UK (n=2), Canada (n=2) and Sweden (n=2). RESULTS: A total of 17 key informants participated in the study. The interview length ranged from 48 to 66 min. 12 theoretical domains populated by 38 subthemes were identified as relevant to measurement feedback systems. The most populous domains included environmental context and resources, memory, attention and decision-making, and social influences. The most populous subthemes included 'quality improvement culture', 'financial and human resource support' and 'patient-centred measurement'. There were minimal conflicting beliefs outside of 'data quality and completeness'. Conflicting beliefs in these subthemes were predominantly between government and clinical leaders. CONCLUSIONS: Multiple factors were found to influence measurement feedback systems and future considerations are presented within this manuscript. The barriers and enablers that impact these systems are complex. While there are some clear modifiable factors in the design of measurement and feedback processes, influential factors described by key informants were largely socioenvironmental. Evidence-based design and implementation, coupled with a deeper understanding of the implementation context, may lead to enhanced quality measurement feedback systems and ultimately improved care delivery and patient outcomes.

Long-Term Outcomes of TROG 13.01 SAFRON II Randomized Trial of Single- Versus Multifraction Stereotactic Ablative Body Radiotherapy for Pulmonary Oligometastases.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.In a randomized phase II clinical trial, the Trans Tasman Radiation Oncology Group compared single- versus multifraction stereotactic ablative body radiotherapy (SABR) in 90 patients with 133 oligometastases to the lung. The study found no differences in safety, efficacy, systemic immunogenicity, or survival between arms, with single-fraction SABR picked as the winner on the basis of cost-effectiveness. In this article, we report the final updated survival outcome analysis. The protocol mandated no concurrent or post-therapy systemic therapy until progression. Modified disease-free survival (mDFS) was defined as any progression not addressable by local therapy, or death. At a median follow-up of 5.4 years, the 3- and 5-year estimates for overall survival (OS) were 70% (95% CI, 59 to 78) and 51% (95% CI, 39 to 61). There were no significant differences between the multi- and single-fraction arms for OS (hazard ratio [HR], 1.1 [95% CI, 0.6 to 2.0]; P = .81). The 3- and 5-year estimates for disease-free survival were 24% (95% CI, 16 to 33) and 20% (95% CI, 13 to 29), with no differences between arms (HR, 1.0 [95% CI, 0.6 to 1.6]; P = .92). The 3- and 5-year estimates for mDFS were 39% (95% CI, 29 to 49) and 34% (95% CI, 24 to 44), with no differences between arms (HR, 1.0 [95% CI, 0.6 to 1.8]; P = .90). In this patient population, where patients receive SABR in lieu of systemic therapy, one-in-three patients are alive without disease in the long term. There were no differences in outcomes by fractionation schedule.

Implementing Smoking Cessation Interventions for Tobacco Users Within Oncology Settings: A Systematic Review.

Importance: Patients with cancer who continue to smoke tobacco experience greater treatment-related complications, higher risk of secondary cancers, and greater mortality. Despite research to improve smoking cessation care within clinical oncology, implementation of proposed interventions within routine care remains challenging. Objective: To identify and recommend implementation strategies for smoking cessation interventions associated with improved screening, advice-giving, and referral for tobacco users recently diagnosed with cancer, as well as shifting smoking behaviors and attitudes in this patient population. Evidence Review: MEDLINE, CINAHL, Embase, and PsycINFO databases, as well as Google Scholar, were searched for articles published before September 7, 2020, using terms related to cancer, smoking cessation, and implementation science. Outcomes of interest were study characteristics, implementation strategies, and outcome measures (screening, advice, referral, abstinence rates, and attitudes). The Cochrane Risk of Bias Tool for randomized and nonrandomized studies was used to assess bias. The review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline and Synthesis Without Meta-analysis (SWiM) guideline. Implementation strategies were categorized according to Expert Recommendations for Implementing Change (ERIC) study taxonomy. A systematic analysis was conducted focusing on studies with low or moderate risk of bias due to high heterogeneity in outcome measurement. Findings: In total, 6047 records were screened, yielding 43 articles (10 randomized clinical trials and 33 nonrandomized studies). Four strategies were associated with improved screening, advice-giving, and referral: (1) supporting clinicians, (2) training implementation stakeholders (including clinicians), (3) changing the infrastructure, and (4) developing stakeholder interrelationships. Conclusions and Relevance: In this systematic review, supporting clinicians by providing cessation care through a trained tobacco specialist was identified as important for achieving short-term abstinence and changing attitudes among patients with cancer. Combined with a theoretical framework and stakeholder involvement, these strategies provide the basis for successful implementation of cessation support; this systematic review serves as an illustration of the methodological application and synthesis of implementation studies and other medical conditions more generally.

Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias.

Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

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.

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.

Patterns of curative treatment for non-small cell lung cancer in New South Wales, Australia.

INTRODUCTION: There is a lack of large population-based studies examining patterns of curative treatment for non-small cell lung cancer (NSCLC) in Australia. This study aimed to evaluate the utilization of curative treatment for NCSLC at a population level and identify factors associated with its use in New South Wales (NSW), Australia. METHODS: Patients diagnosed with localized or locoregional NSCLC between 2009 and 2014 were identified from the NSW Central Cancer Registry. Curative treatment was defined as surgery or radiotherapy with a 45 Gy minimum dose. Univariate and multivariable analyses were performed to investigate factors associated with the receipt of curative treatment. A Cox proportional-hazards regression model was used to analyze the factors associated with 2-year overall survival (OS). RESULTS: Of the 5722 patients diagnosed with NSCLC in the study period, 3355 (59%) patients received curative treatment and 2367 (41%) patients did not receive curative treatment. The receipt of curative treatment was significantly associated with younger patients, female gender, localized disease, and Charlson Comorbidity Index (CCI) = 0. The use of curative treatment increased significantly over time from 2009 (55%) to 2014 (63%) and varied significantly from 24% to 70% between local health districts (LHDs) of residence. Younger age, female gender, localized disease, CCI = 0, and overseas country of birth were significantly associated with 2-year OS. The 2-year OS significantly improved from 70% in 2009 to 77% in 2014 for patients who received curative treatment. CONCLUSION: The use of curative treatment for patients with potentially curable NSCLC was low at 59%. However, the use of curative treatment and survival have increased over time. Significant variation was noted in the use of curative treatment between LHDs.

Building staff capability, opportunity, and motivation to provide smoking cessation to people with cancer in Australian cancer treatment centres: development of an implementation intervention framework for the Care to Quit cluster randomised controlled trial.

Few rigorous studies provide a clear description of the methodological approach of developing an evidence-based implementation intervention, prior to implementation at scale. This study describes the development, mapping, rating, and review of the implementation strategies for the Care to Quit smoking cessation trial, prior to application in nine cancer services across Australia. Key stakeholders were engaged in the process from conception through to rating, reviewing and refinement of strategies and principles. An initial scoping review identified 21 barriers to provision of evidence-based smoking cessation care to patients with cancer, which were mapped to the Theoretical Domains Framework and Behaviour Change Wheel (BCW) to identify relevant intervention functions. The mapping identified 26 relevant behaviour change techniques, summarised into 11 implementation strategies. The implementation strategies were rated and reviewed against the BCW Affordability, Practicality, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria by key stakeholders during two interactive workshops to facilitate a focus on feasible interventions likely to resonate with clinical staff. The implementation strategies and associated intervention tools were then collated by form and function to provide a practical guide for implementing the intervention. This study illustrates the rigorous use of theories and frameworks to arrive at a practical intervention guide, with potential to inform future replication and scalability of evidence-based implementation across a range of health service settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s10742-022-00288-6.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer.

INTRODUCTION: Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS: An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS: The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION: The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.

Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer.

Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.

Development of an Australia and New Zealand Lung Cancer Clinical Quality Registry: a protocol paper.

INTRODUCTION: Lung cancer is the leading cause of cancer mortality, comprising the largest national cancer disease burden in Australia and New Zealand. Regional reports identify substantial evidence-practice gaps, unwarranted variation from best practice, and variation in processes and outcomes of care between treating centres. The Australia and New Zealand Lung Cancer Registry (ANZLCR) will be developed as a Clinical Quality Registry to monitor the safety, quality and effectiveness of lung cancer care in Australia and New Zealand. METHODS AND ANALYSIS: Patient participants will include all adults >18 years of age with a new diagnosis of non-small-cell lung cancer (NSCLC), SCLC, thymoma or mesothelioma. The ANZLCR will register confirmed diagnoses using opt-out consent. Data will address key patient, disease, management processes and outcomes reported as clinical quality indicators. Electronic data collection facilitated by local data collectors and local, state and federal data linkage will enhance completeness and accuracy. Data will be stored and maintained in a secure web-based data platform overseen by registry management. Central governance with binational representation from consumers, patients and carers, governance, administration, health department, health policy bodies, university research and healthcare workers will provide project oversight. ETHICS AND DISSEMINATION: The ANZLCR has received national ethics approval under the National Mutual Acceptance scheme. Data will be routinely reported to participating sites describing performance against measures of agreed best practice and nationally to stakeholders including federal, state and territory departments of health. Local, regional and (bi)national benchmarks, augmented with online dashboard indicator reporting will enable local targeting of quality improvement efforts.

Stepping into the real world: a mixed-methods evaluation of the implementation of electronic patient reported outcomes in routine lung cancer care.

BACKGROUND: To realize the broader benefits of electronic patient-reported outcome measures (ePROMs) in routine care, we used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to inform the translation of a clinically effective ePROM system (hereafter referred to as the PRM system) into practice. The study aimed to evaluate the processes and success of implementing the PRM system in the routine care of patients diagnosed with lung cancer. METHOD: A controlled before-and-after mixed-methods study was undertaken. Data sources included a self-report questionnaire and interviews with healthcare providers, electronic health record data for PRMs patients and historical controls, and field notes. Descriptive statistics, logistic regression modelling, negative binomial models, generalized estimating equations and repeated measures ANOVA were used to analyze quantitative data. Qualitative data was thematically analyzed. RESULTS: A total of 48/79 eligible people diagnosed with lung cancer completed 90 assessments during the 5-month implementation period (RE-AIM reach). Every assessment breached the pre-defined threshold and care coordinators reviewed and actioned 95.6% of breaches, resulting in 146 referrals to allied health services, most frequently for social work (25.3%), dietetics (18.5%), physiotherapy (18.5%) and occupational therapy (17.1%). PRMs patients had significantly fewer visits to the cancer assessment unit for problematic symptoms (M = 0.23 vs. M = 0.43; p = 0.035), and were significantly more likely to be offered referrals (71% vs. 29%, p < 0.0001) than historical controls (RE-AIM effect). The levels of 'organizational readiness for implementing change' (ORIC) did not show much differences between baseline and follow-up, though this was already high at baseline; but significantly more staff reported improved confidence when asking patients to complete assessments (64.7% at baseline vs. 88.2% at follow-up, p = 0.0046), and when describing the assessment tool to patients (64.7% at baseline vs. 76.47% at follow-up, p = 0.0018) (RE-AIM adoption). A total of 78 staff received PRM system training, and 95.6% of the PRM system alerts were actioned (RE-AIM implementation); and all lung cancer care coordinators were engaged with the PRM system beyond the end of the study period (RE-AIM maintenance). CONCLUSION: This study demonstrates the potential of the PRM system in enhancing the routine care of lung cancer patients, through leveraging the capabilities of automated web-based care options. Research has shown the clear benefits of using electronically collected patient-reported outcome measures (ePROMs) for cancer patients and health services. However, we need to better understand how to implement ePROMs as part of routine care. This study evaluated the processes and outcomes of implementing an ePROMs system in the routine care of patients diagnosed with lung cancer. Key findings included: (a) a majority of eligible patients completed the scheduled assessments; (b) patient concerns were identified in every assessment, and care coordinators reviewed and actioned almost all of these, including making significantly more referrals to allied health services; (c) patients completing assessments regularly were less likely to present to the cancer assessment unit with problematic symptoms, suggesting that ePROMs identified patient concerns early and this led to a timely response to concerns; (d) staff training and engagement was high, and staff reporting increased confidence when asking patients to complete assessments and when describing the assessment tool to patients at the end of the implementation period. This study shows that implementing ePROMs in routine care is feasible and can lead to improvements in patient care.