Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy

AIMS

Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer.

MATERIALS AND METHODS

Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison.

RESULTS

Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient.

CONCLUSION

Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the 'dose range' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.

Interspecies encounters with endemic health conditions: co-producing BVD and lameness with cows and sheep in the north of England

This paper focuses on the relationships between people and farmed nonhuman animals, and between these animals and the farmed environments they encounter, in the enactment of interspecies endemic disease situations. It examines how the nonhuman embodied capacities, agency and subjectivities of cows and sheep on farms in the north of England make a difference to how the endemic conditions of lameness and bovine viral diarrhoea (BVD) are encountered and responded to by farmers and advisers. The paper draws on empirical research with farmers and their advisers, and explores three key, inter-related, themes: first, the importance of intersubjective relationships between people and animals on farms; second, the nonhuman components of the 'disease situations' associated with endemic diseases, including animals' embodied characteristics and behaviours and the relationships between bodies and environments on different farms; and finally the ways in which animal agency and resistance makes a difference to on-farm interventions aiming to prevent or treat lameness and BVD. The paper concludes by arguing that animals' capacities, and nonhuman difference, should be taken further into account in future policy and practice interventions in endemic disease in farmed animals.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell Lung Cancer Using Real-World Data

AIMS

The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy.

METHODS

A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups.

RESULTS

1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01).

CONCLUSION

Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.

Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images

Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, delineation and quantification of bone metastases using FDG-PET/CT images. The tool extracts standardised uptake values (SUV) from FDG-PET images for auto-segmentation of bone lesions and calculates volume of each lesion and associated mean and maximum SUV. The tool also allows automatic statistical validation of the auto-segmented bone lesions against the manual contours of a radiation oncologist. A retrospective review of FDG-PET/CT scans of more than 30 candidate NSCLC patients was performed and nine patients with one or more metastatic bone lesions were selected for the present study. The SUV threshold prediction model was designed by splitting the cohort of patients into a subset of 'development' and 'validation' cohorts. The development cohort yielded an optimum SUV threshold of 3.0 for automatic detection of bone metastases using FDG-PET/CT images. The validity of the derived optimum SUV threshold on the validation cohort demonstrated that auto-segmented and manually contoured bone lesions showed strong concordance for volume of bone lesion (r = 0.993) and number of detected lesions (r = 0.996). The tool has various applications in radiotherapy, including but not limited to studies determining optimum SUV threshold for accurate and standardised delineation of bone lesions and in scientific studies utilising large patient populations for instance for investigation of the number of metastatic lesions that can be treated safety with an ablative dose of radiotherapy without exceeding the normal tissue toxicity.

Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy

BACKGROUND AND PURPOSE

Accurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with potential application in clinical settings and large-scale radiation-related cardiotoxicity studies.

MATERIALS AND METHODS

A recently developed hybrid method for automatic segmentation of 18 cardiac structures, combining deep learning, multi-atlas mapping and geometric segmentation of small challenging substructures, was independently validated on 30 lung cancer cases. These included anatomical and imaging variations, such as tumour abutting heart, lung collapse and metal artefacts. Automatic segmentations were compared with manual contours of the 18 structures using quantitative metrics, including Dice similarity coefficient (DSC), mean distance to agreement (MDA) and dose comparisons.

RESULTS

A comparison of manual and automatic contours across all cases showed a median DSC of 0.75-0.93 and a median MDA of 2.09-3.34 mm for whole heart and chambers. The median MDA for great vessels, coronary arteries, cardiac valves, sinoatrial and atrioventricular conduction nodes was 3.01-8.54 mm. For the 27 cases treated with curative intent (planned target volume dose ≥50 Gy), the median dose difference was -1.12 to 0.57 Gy (absolute difference of 1.13-3.25%) for the mean dose to heart and chambers; and -2.25 to 4.45 Gy (absolute difference of 0.94-6.79%) for the mean dose to substructures.

CONCLUSION

The novel hybrid automatic segmentation tool reported high accuracy and consistency over a validation set with challenging anatomical and imaging variations. This has promising applications in substructure dose calculations of large-scale datasets and for future studies on long-term cardiac toxicity.

Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study

BACKGROUND

No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge.

METHODS

The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing.

FINDINGS

2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters.

INTERPRETATION

The sequelae of a hospital admission with COVID-19 were substantial 1 year after discharge across a range of health domains, with the minority in our cohort feeling fully recovered. Patient-perceived health-related quality of life was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials.

FUNDING

UK Research and Innovation and National Institute for Health Research.

Mid-treatment Fluorodeoxyglucose Positron Emission Tomography in Human Papillomavirus-related Oropharyngeal Squamous Cell Carcinoma Treated with Primary Radiotherapy: Nodal Metabolic Response Rate can Predict Treatment Outcomes

AIMS

To evaluate whether biomarkers derived from fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) performed prior to (prePET) and during the third week (interim PET; iPET) of radiotherapy can predict treatment outcomes in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPC).

MATERIALS AND METHODS

This retrospective analysis included 46 patients with newly diagnosed OPC treated with definitive (chemo)radiation and all patients had confirmed positive HPV status (HPV+OPC) based on p16 immunohistochemistry. The maximum standardised uptake value (SUV_max), metabolic tumour volume (MTV) and total lesional glycolysis (TLG) of primary, index node (node with the highest TLG) and total lymph nodes and their median percentage (≥50%) reductions in iPET were analysed, and correlated with 5-year Kaplan-Meier and multivariable analyses (smoking, T4, N2b-3 and AJCC stage IV), including local failure-free survival, regional failure-free survival, locoregional failure-free survival (LRFFS), distant metastatic failure-free survival (DMFFS), disease-free survival (DFS) and overall survival.

RESULTS

There was no association of outcomes with prePET parameters observed on multivariate analysis. A complete metabolic response of primary tumour was seen in 13 patients; the negative predictive value for local failure was 100%. More than a 50% reduction in total nodal MTV provided the best predictor of outcomes, including LRFFS (88% versus 47.1%, P = 0.006, hazard ratio = 0.153) and DFS (78.2% versus 41.2%, P = 0.01, hazard ratio = 0.234). More than a 50% reduction in index node TLG was inversely related to DMFFS: a better nodal response was associated with a higher incidence of distant metastatic failure (66.7% versus 100%, P = 0.009, hazard ratio = 3.0).

CONCLUSION

The reduction (≥50%) of volumetric nodal metabolic burden can potentially identify a subgroup of HPV+OPC patients at low risk of locoregional failure but inversely at higher risk of distant metastatic failure and may have a role in individualised adaptive radiotherapy and systemic therapy.

Regional cardiac dysfunction determined by radiation dose in patients with breast cancer

Background

Subclinical left ventricular (LV) dysfunction by 2D global longitudinal strain (GLS) immediately following radiotherapy (RT) and persisting at 12 months has been described in breast cancer (BC) patients. We hypothesised that persistent LV dysfunction may be regional and correlate with segmental RT.

Methods

Transthoracic echocardiograms were performed at baseline, 6 weeks and 12 months post-RT on 61 chemotherapy-naïve women with left sided BC.

Results

Anterior and anteroseptal regions received the highest radiation dose, and posterior and inferior segments the lowest radiation dose (Figure 1). Within each region, there was a progressive increase in the radiation dose received from base to apex. At 6 weeks, the greatest reduction in strain was observed in the anterior and anteroseptal regions, with the most significant reduction in strain in the apical segments. At 12 months, despite improvement in strain, the percentage reduction in strain was similar. There was a significant interaction between both region and segment, on the percentage change in strain at 6 weeks (p&lt;0.001) and at 12 months (p=0.007). Pairwise comparisons of apical to basal percentage change in strain demonstrated the most significant reductions in the anterior region at 6 weeks and 12 months (Table 1).

Conclusions

RT causes segmental myocardial dysfunction, with areas receiving the highest RT demonstrating the largest impairment in strain, with these changes persisting at 12 months. Long term correlation with adverse events is required.

Figure 1. Radiation dose by region and segment

Funding Acknowledgement

Type of funding source: None

Technical Note: Experimental results from a prototype high-field inline MRI-linac

PURPOSE

The pursuit of real-time image guided radiotherapy using optimal tissue contrast has seen the development of several hybrid magnetic resonance imaging (MRI)-treatment systems, high field and low field, and inline and perpendicular configurations. As part of a new MRI-linac program, an MRI scanner was integrated with a linear accelerator to enable investigations of a coupled inline MRI-linac system. This work describes results from a prototype experimental system to demonstrate the feasibility of a high field inline MR-linac.

METHODS

The magnet is a 1.5 T MRI system (Sonata, Siemens Healthcare) was located in a purpose built radiofrequency (RF) cage enabling shielding from and close proximity to a linear accelerator with inline (and future perpendicular) orientation. A portable linear accelerator (Linatron, Varian) was installed together with a multileaf collimator (Millennium, Varian) to provide dynamic field collimation and the whole assembly built onto a stainless-steel rail system. A series of MRI-linac experiments was performed to investigate (1) image quality with beam on measured using a macropodine (kangaroo) ex vivo phantom; (2) the noise as a function of beam state measured using a 6-channel surface coil array; and (3) electron contamination effects measured using Gafchromic film and an electronic portal imaging device (EPID).

RESULTS

(1) Image quality was unaffected by the radiation beam with the macropodine phantom image with the beam on being almost identical to the image with the beam off. (2) Noise measured with a surface RF coil produced a 25% elevation of background intensity when the radiation beam was on. (3) Film and EPID measurements demonstrated electron focusing occurring along the centerline of the magnet axis.

CONCLUSIONS

A proof-of-concept high-field MRI-linac has been built and experimentally characterized. This system has allowed us to establish the efficacy of a high field inline MRI-linac and study a number of the technical challenges and solutions.