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Aim: Previous research has shown patients and the public in Australia generally support medical researchers in making de‐identified research data available to other scientists. However, this research has focussed on certain types of data and recipients. We surveyed Australians affected by cancer to characterize their attitudes toward the sharing of research data with multiple third parties, including the public. Methods: A short, anonymous online survey of Australians with a previous diagnosis of cancer was advertised between October 27, 2022, and February 27, 2023. Quantitative responses were analyzed with descriptive statistics. Free‐text responses were coded deductively and summarised using content analysis. Results: In total, 551 respondents contributed data to the survey. There was strong support for cancer researchers sharing non‐human and de‐identified human research data with clinicians (90% and 95%, respectively) and non‐profit researchers (both 94%). However, fewer participants supported sharing data with for‐profit researchers (both 64%) or publicly (both 61%). When asked if they would hypothetically consent to researchers at their treatment location using and sharing their de‐identified data publicly, only half agreed. In contrast, after being shown a visual representation of the de‐identified survey data, 80% of respondents supported sharing it publicly. Conclusion: Australians affected by cancer support the sharing of research data, particularly with clinicians and non‐profit researchers. Our results also imply that visualization of the data to be shared may enhance support for making it publicly available. These results should help alleviate any concerns about research participants' attitudes toward data sharing, as well as boost researchers' motivation for sharing. [ABSTRACT FROM AUTHOR]

Delivering radiotherapy to patients in an upright position can allow for increased patient comfort, reduction in normal tissue irradiation, or reduction of machine size and complexity. This paper gives an overview of the requirements for the delivery of contemporary arc and modulated radiation therapy to upright patients. We explore i) patient positioning and immobilization, ii) simulation imaging, iii) treatment planning and iv) online setup and image guidance. Treatment chairs have been designed to reproducibly position seated patients for treatment and can be augmented by several existing immobilisation systems or promising emerging technologies such as soft robotics. There are few solutions for acquiring CT images for upright patients, however, cone beam computed tomography (CBCT) scans of upright patients can be produced using the imaging capabilities of standard Linacs combined with an additional patient rotation device. While these images will require corrections to make them appropriate for treatment planning, several methods indicate the viability of this approach. Treatment planning is largely unchanged apart from translating gantry rotation to patient rotation, allowing for a fixed beam with a patient rotating relative to it. Rotation can be provided by a turntable during treatment delivery. Imaging the patient with the same machinery as used in treatment could be advantageous for online plan adaption. While the current focus is using clinical linacs in existing facilities, developments in this area could also extend to lower-cost and mobile linacs and heavy ion therapy. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to investigate whether nodal metabolic tumour volume (nMTV) and nodal total lesion glycolysis (nTLG) on Fluorine‐18 fluoro‐deoxy‐glucose positron emission tomography–computed tomography (18F‐FDG PET/CT) in inoperable node‐positive stage II and III non‐small cell lung cancer (NSCLC) are independent predictors of overall survival (OS) in patients undergoing curative‐intent chemoradiotherapy/radiotherapy (CRT/RT). Methods: Data from two prospective trials between 2004 and 2016 were analysed retrospectively. Primary, nodal and total metabolic tumour volume and total lesion glycolysis (pMTV, nMTV, tMTV, pTLG, nTLG and tTLG, respectively) were derived from baseline 18F‐FDG PET/CT. Cox regressions were used to model OS by 18F‐FDG PET/CT parameters adjusting for overall stage. Results: 89 patients with stage II (8%) and stage III (92%) were included. The median age at diagnosis was 67 years; 62% were male. The median follow‐up was 6.9 years; the median OS was 2.2 years (95% CI 1.7–3.1). The median pMTV, nMTV and tMTV were 14 mL (range 0–360), 8 mL (range 0–250) and 34 mL (range 3–384), respectively. In 3 patients, the primary lesion could not be delineated from the central hilar mass. There was no association between nMTV (adjusted HR 1.04, 95% CI 0.95–1.15, P‐value 0.43), pMTV (adjusted HR 1.0, 95% CI 0.96–1.04, P‐value 0.92), tMTV (adjusted HR 1.0, 95% CI 0.97–1.04, P‐value 0.88), nTLG, pTLG or tTLG and OS. Consistent results were noted when patients with central hilar lesions were excluded from analysis. Conclusion: In node‐positive stage II and III NSCLC patients who underwent 18F‐FDG PET/CT‐guided target delineation curative‐intent concurrent CRT/RT, metabolic parameters did not appear to provide independent prognostication. [ABSTRACT FROM AUTHOR]

In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients. [ABSTRACT FROM AUTHOR]