Effect of systemic inflammation biomarkers on overall survival after lung cancer radiotherapy: a single-center large-cohort study.

INTRODUCTION: Recent studies suggest that immune-related cells can be recruited for anti-tumor functions as well as tumor progression and the interplay between systemic inflammation and local immune response may play a major role in the development and progression of various cancers including lung cancer. Inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) can be used as surrogate biomarkers of host immune status. In this work, associations between neutrophils, lymphocytes, platelets, NLR, PLR, SII and overall survival (OS) are investigated in two cohorts of non-small cell lung cancer (NSCLC) patients treated with fractionated radiotherapy (RT) and stereotactic body radiation therapy (SBRT) and a cohort of small cell lung cancer (SCLC) patients treated with fractionated RT. MATERIAL AND METHODS: Data from 2513 lung cancer patients were retrospectively analyzed. Baseline NLR, PLR, and SII (NLR × platelet count) were calculated from full blood test prior to RT initiation. Cox proportional hazards regression analyses were used to evaluate the association between systemic inflammation markers and known clinical factors with OS. RESULTS: The two-year OS was 42%, 63%, and 62% in the NSCLC fractionated RT, SBRT, and SCLC cohort. NLR (per 1 unit: hazard ratio [HR]: 1.04, p < 0.05) and SII (per 100 × 109/L: HR: 1.01, p < 0.05) remained the strongest independent factors of OS in multivariable Cox analyses, correcting for clinical factors in early-stage and locally advanced NSCLC and SCLC patients treated with RT. DISCUSSION: This single-center large-cohort study suggests that baseline NLR and SII are independent prognostic biomarkers associated with OS in locally advanced and early-stage NSCLC patients treated with either curative-intent fractionated RT or SBRT and SCLC patients treated with curative-intent fractionated RT. External validation is warranted to evaluate the utility of these biomarkers for patients' stratification and adapting new treatment approaches.

CERN-based experiments and Monte-Carlo studies on focused dose delivery with very high energy electron (VHEE) beams for radiotherapy applications.

Very High Energy Electron (VHEE) beams are a promising alternative to conventional radiotherapy due to their highly penetrating nature and their applicability as a modality for FLASH (ultra-high dose-rate) radiotherapy. The dose distributions due to VHEE need to be optimised; one option is through the use of quadrupole magnets to focus the beam, reducing the dose to healthy tissue and allowing for targeted dose delivery at conventional or FLASH dose-rates. This paper presents an in depth exploration of the focusing achievable at the current CLEAR (CERN Linear Electron Accelerator for Research) facility, for beam energies >200 MeV. A shorter, more optimal quadrupole setup was also investigated using the TOPAS code in Monte Carlo simulations, with dimensions and beam parameters more appropriate to a clinical situation. This work provides insight into how a focused VHEE radiotherapy beam delivery system might be achieved.

An Overview of Real-World Data Infrastructure for Cancer Research.

AIMS: There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research. MATERIALS AND METHODS: There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales. RESULTS: For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology. CONCLUSION: There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

Validation of deformable registration in head and neck cancer using analysis of variance.

PURPOSE: Deformable image registration (DIR) is often validated based on a distance-to-agreement (DTA) criterion of automatically propagated anatomical landmarks that were manually identified. Due to human observer variability, however, the performance of the registration method is diluted. The purpose of this study was to evaluate an analysis of variance (ANOVA) based validation to account for such observer variation. METHODS: Weekly cone beam CTs (CBCTs) of ten head and neck cancer patients undergoing five weeks of radiotherapy were used. An expert identified 23 anatomical features (landmarks) on the planning CT. The landmarks were automatically propagated to the CBCT using multiregion-of-interest (mROI) registration. Additionally, two human observers independently localized these landmarks on the CBCTs. Subsequently, ANOVA was used to compute the variance of each observer on the pairwise distance (PWD). RESULTS: ANOVA based analysis demonstrated that a classical DTA approach underestimated the precision for the mROI due to human observer variation by about 25%. The systematic error (accuracy) of mROI ranged from 0.13 to 0.17 mm; the variability (1 SD) (precision) ranged from 1.3 to 1.5 mm demonstrating that its performance is dominated by the precision. CONCLUSIONS: The PWD-ANOVA method accounts for human observer variation allowing a better estimation of the of DIR errors.

A novel use for routine CBCT imaging during radiotherapy to detect COVID-19.

INTRODUCTION: Thoracic CT is a useful tool in the early diagnosis of patients with COVID-19. Typical appearances include patchy ground glass shadowing. Thoracic radiotherapy uses daily cone beam CT imaging (CBCT) to check for changes in patient positioning and anatomy prior to treatment through a qualitative assessment of lung appearance by radiographers. Observation of changes related to COVID-19 infection during this process may facilitate earlier testing improving patient management and staff protection. METHODS: A tool was developed to create overview reports for all CBCTs for each patient throughout their treatment. Reports contain coronal maximum intensity projection (MIP's) of all CBCTs and plots of lung density over time. A single therapeutic radiographer undertook a blinded off-line audit that reviewed 150 patient datasets for tool optimisation in which medical notes were compared to image findings. This cohort included 75 patients treated during the pandemic and 75 patients treated between 2014 and 2017. The process was repeated retrospectively on a subset of the 285 thoracic radiotherapy patients treated between January-June 2020 to assess the efficiency of the tool and process. RESULTS: Three patients in the n = 150 optimisation cohort had confirmed COVID-19 infections during their radiotherapy. Two of these were detected by the reported image assessment process. The third case was not detected on CBCT due to minimal density changes in the visible part of the lungs. Within the retrospective cohort four patients had confirmed COVID-19 based on RT-PCR tests, three of which were retrospectively detected by the reported process. CONCLUSION: The preliminary results indicate that the presence of COVID-19 can be detected on CBCT by therapeutic radiographers. IMPLICATIONS FOR PRACTICE: This process has now been extended to clinical service with daily assessments of all thoracic CBCTs. Changes noted are referred for oncologist review.

Clinico-pathological predictors of clinical complete response in rectal cancer.

PURPOSE: Prediction of clinical complete response in rectal cancer before neoadjuvant chemo-radiotherapy treatment enables treatment selection. Patients predicted to have complete response could have chemo-radiotherapy, and others could have additional doublet chemotherapy at this stage of their treatment to improve their overall outcome. This work investigates the role of clinical variables in predicting clinical complete response. METHOD: Using the UK-based OnCoRe database (2008 to 2019), we performed a propensity-score matched study of 322 patients who received neoadjuvant chemoradiotherapy. We collected pre-treatment clinic-pathological, inflammatory and radiotherapy-related characteristics. We determined the odds for the occurrence of cCR using conditional logistic regression models. We derived the post-model Area under the Curve (AUC) as an indicator of discrimination performance and stated a priori that an AUC of 0.75 or greater was required for potential clinical utility. RESULTS: Pre-treatment tumour diameter, mrT-stage, haemoglobin, alkaline phosphate and total radiotherapy depths were associated with cCR on univariable and multivariable analysis. Additionally, neutrophil to lymphocyte ratio (NLR), neutrophil-monocyte to lymphocyte ratio (NMLR), lymphocyte count and albumin were all significantly associated with cCR on multivariable analysis. A nomogram using the above parameters was developed with a resulting ROC AUC of 0.75. CONCLUSION: We identified routine clinic-pathological, inflammatory and radiotherapy-related variables which are independently associated with cCR. A nomogram was developed to predict cCR. The performance characteristics from this model were on the prior clinical utility threshold. Additional research is required to develop more associated variables to better select patients with rectal cancer undergoing chemoradiotherapy who may benefit from pursuing a W&W strategy.

Serotonin and dopamine transporters in relation to neuropsychological functioning, personality traits and mood in young adult healthy subjects.

BACKGROUND: Serotonin and dopamine neurotransmitter systems are implicated in the regulation of mood, cognition and personality traits and their dysfunction is thought to be implicated in diverse psychopathologies. However, in healthy subjects the relationship between the serotonin and dopamine systems and neuropsychological functioning and personality traits is not clearly established. In the present study we investigated whether neuropsychological functioning, personality traits and mood states of a group of healthy subjects are associated with in vivo measures of serotonin transporters (SERTs) and dopamine transporters (DATs). METHOD: A total of 188 young healthy subjects underwent neuropsychological and subjective measurements of memory function, depression and impulsivity. Participants' SERT and DAT availability in predefined regions of interest were assessed using single photon emission computed tomography (SPECT) with the radiotracer [123I]ß-CIT. Individual magnetic resonance imaging (MRI) scans served as anatomic reference. RESULTS: We did not find any significant association between SERT or DAT availability and neuropsychological test performance or self-reported impulsivity and mood. There were no significant sex differences in SERT or DAT availability, but men performed significantly better on some tests of visuospatial functioning than women. CONCLUSIONS: Robust negative findings for striatal DAT availability seriously question earlier findings of positive associations between DAT availability and cognitive functions in healthy subjects. Our results also suggest that subcortical SERT availability is not associated with the neuropsychological functions and personality traits assessed. In summary, the present study suggests that neuropsychological and personality measurements in young healthy people are not associated with subcortical SERT or striatal DAT availabilities in the brain.

Semi-automatic delineation using weighted CT-MRI registered images for radiotherapy of nasopharyngeal cancer.

PURPOSE: To develop a delineation tool that refines physician-drawn contours of the gross tumor volume (GTV) in nasopharynx cancer, using combined pixel value information from x-ray computed tomography (CT) and magnetic resonance imaging (MRI) during delineation. METHODS: Operator-guided delineation assisted by a so-called "snake" algorithm was applied on weighted CT-MRI registered images. The physician delineates a rough tumor contour that is continuously adjusted by the snake algorithm using the underlying image characteristics. The algorithm was evaluated on five nasopharyngeal cancer patients. Different linear weightings CT and MRI were tested as input for the snake algorithm and compared according to contrast and tumor to noise ratio (TNR). The semi-automatic delineation was compared with manual contouring by seven experienced radiation oncologists. RESULTS: A good compromise for TNR and contrast was obtained by weighing CT twice as strong as MRI. The new algorithm did not notably reduce interobserver variability, it did however, reduce the average delineation time by 6 min per case. CONCLUSIONS: The authors developed a user-driven tool for delineation and correction based a snake algorithm and registered weighted CT image and MRI. The algorithm adds morphological information from CT during the delineation on MRI and accelerates the delineation task.

Focused VHEE (very high energy electron) beams and dose delivery for radiotherapy applications.

This paper presents the first demonstration of deeply penetrating dose delivery using focused very high energy electron (VHEE) beams using quadrupole magnets in Monte Carlo simulations. We show that the focal point is readily modified by linearly changing the quadrupole magnet strength only. We also present a weighted sum of focused electron beams to form a spread-out electron peak (SOEP) over a target region. This has a significantly reduced entrance dose compared to a proton-based spread-out Bragg peak (SOBP). Very high energy electron (VHEE) beams are an exciting prospect in external beam radiotherapy. VHEEs are less sensitive to inhomogeneities than proton and photon beams, have a deep dose reach and could potentially be used to deliver FLASH radiotherapy. The dose distributions of unfocused VHEE produce high entrance and exit doses compared to other radiotherapy modalities unless focusing is employed, and in this case the entrance dose is considerably improved over existing radiations. We have investigated both symmetric and asymmetric focusing as well as focusing with a range of beam energies.

The Impact of Intra-thoracic Anatomical Changes upon the Delivery of Lung Stereotactic Ablative Radiotherapy.

AIMS: So far, the impact of intra-thoracic anatomical changes (ITACs) on patients treated with stereotactic ablative radiotherapy (SABR) for early-stage non-small cell lung cancer is unknown. Studying these is important, as ITACs have the potential to impact the workflow and reduce treatment quality. The aim of this study was to assess and categorise ITACs, as detected on cone beam computed tomography scans (CBCT), and their subsequent impact upon treatment in lung cancer patients treated with SABR. MATERIALS AND METHODS: CBCTs from 100 patients treated with SABR for early non-small cell lung cancer were retrospectively reviewed. The presence of the following ITACs was assessed: atelectasis, infiltrative change, pleural effusion, baseline shift and gross tumour volume (GTV) increase and decrease. ITACs were graded using a traffic light protocol. This was adapted from a tool previously developed to assesses potential target undercoverage or organ at risk overdose. The frequency of physics or clinician review was noted. A linear mixed effects model was used to assess the relationship between ITAC grade and set-up time (time from first CBCT to beam delivery). RESULTS: ITACs were observed in 22% of patients. Twenty-one per cent of these were categorised as 'red', implying a risk of underdosage to the GTV. Most were 'yellow' (51%), indicating little impact upon planning target volume coverage of the GTV. Physics or clinician review was required in 10% of all treatment fractions overall. Three patients needed their treatment replanned. The mixed effect model analysis showed that ITACs cause a significant prolongation of set-up time (Χ2(3) = 9.22, P = 0.02). CONCLUSION: Most ITACs were minor, but associated with unplanned physics or clinician review, representing a potentially significant resource burden. ITACs also had a significant impact upon set-up time, with consequences for the wider workflow and intra-fraction motion. Detailed guidance on the management of ITACs is needed to provide support for therapeutic radiographers delivering lung SABR.

An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.

INTRODUCTION: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy. METHODS: Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (modelCT) and an MR model (modelMRI). Models were trained to generate auto contours for the bilateral parotid glands and submandibular glands. Auto-contours for modelMRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans and, by modelCT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC) and distance to agreement (DTA) were calculated for comparison. RESULTS: ModelMRI contours improved the mean DSC and DTA compared with manual contours for the bilateral parotid glands and submandibular glands on the diagnostic and RTP MRs compared with the MRL sequence. There were statistically significant differences seen for modelMRI compared to modelCT for the left parotid (mean DTA 2.3 v 2.8 mm), right parotid (mean DTA 1.9 v 2.7 mm), left submandibular gland (mean DTA 2.2 v 2.4 mm) and right submandibular gland (mean DTA 1.6 v 3.2 mm). CONCLUSION: A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.

Inter- and intra-fractional stability of rectal gas in pelvic cancer patients during MRIgRT.

PURPOSE: Due to the electron return effect (ERE) during magnetic resonance imaging guided radiotherapy (MRIgRT), rectal gas during pelvic treatments can result in hot spots of over-dosage in the rectal wall. Determining the clinical impact of this effect on rectal toxicity requires estimation of the amount and mobility (and stability) of rectal gas during treatment. We therefore investigated the amount of rectal gas and local inter- and intra-fractional changes of rectal gas in pelvic cancer patients. METHODS: To estimate the volume of gas present at treatment planning, the rectal gas contents in the planning computed tomography (CT) scans of 124 bladder, 70 cervical and 2180 prostate cancer patients were calculated. To estimate inter- and intra-fractional variations in rectal gas, 174 and 131 T2-w MRIs for six cervical and eleven bladder cancer patients were used. These scans were acquired during four scan-sessions (~20-25 min each) at various time-points. Additionally, 258 T2-w MRIs of the first five prostate cancer patients treated using MRIgRT at our center, acquired during each fraction, were analyzed. Rectums were delineated on all scans. The area of gas within the rectum delineations was identified on each MRI slice using thresholding techniques. The area of gas on each slice of the rectum was used to calculate the inter- and intra-fractional group mean, systematic and random variations along the length of the rectum. The cumulative dose perturbation as a result of the gas was estimated. Two approaches were explored: accounting or not accounting for the gas at the start of the scan-session. RESULTS: Intra-fractional variations in rectal gas are small compared to the absolute volume of rectal gas detected for all patient groups. That is, rectal gas is likely to remain stable for periods of 20-25 min. Larger volumes of gas and larger variations in gas volume were observed in bladder cancer patients compared with cervical and prostate cancer patients. For all patients, local cumulative dose perturbations per beam over an entire treatment in the order of 60 % were estimated when gas had not been accounted for in the daily adaption. The calculated dose perturbation over the whole treatment was dramatically reduced in all patients when accounting for the gas in the daily set-up image. CONCLUSION: Rectal gas in pelvic cancer patients is likely to remain stable over the course of an MRIgRT fraction, and also likely to reappear in the same location in multiple fractions, and can therefore result in clinically relevant over-dosage in the rectal wall. The over-dosage is reduced when accounting for gas in the daily adaption.

Catching errors with in vivo EPID dosimetry.

The potential for detrimental incidents and the ever increasing complexity of patient treatments emphasize the need for accurate dosimetric verification in radiotherapy. For this reason, all curative treatments are verified, either pretreatment or in vivo, by electronic portal imaging device (EPID) dosimetry in the Radiation Oncology Department of The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands. Since the clinical introduction of the method in January 2005 until August 2009, treatment plans of 4337 patients have been verified. Among these plans, 17 serious errors were detected that led to intervention. Due to their origin, nine of these errors would not have been detected with pretreatment verification. The method is illustrated in detail by the case of a plan transfer error detected in a 5 x 5 Gy intensity-modulated radiotherapy (IMRT) rectum treatment. The EPID reconstructed dose at the isocenter was 6.3% below the planned value. Investigation of the plan transfer chain revealed that due to a network transfer error, the plan was corrupted. 3D analysis of the acquired EPID data revealed serious underdosage of the planning target volume: On average 11.6%, locally up to 20%. This report shows the importance of in vivo (EPID) dosimetry for all treatment plans as well as the ability of the method to assess the dosimetric impact of deviations found.

Impact of Peer Review in Reducing Uncertainty in the Definition of the Lung Target Volume Among Trainee Oncologists.

AIMS: To evaluate the impact of peer review and contouring workshops on reducing uncertainty in target volume delineation for lung cancer radiotherapy. MATERIALS AND METHODS: Data from two lung cancer target volume delineation courses were analysed. In total, 22 trainees in clinical oncology working across different UK centres attended these courses with priori experience in lung cancer radiotherapy. The courses were made up of short presentations and contouring practice sessions. The participants were divided into two groups and asked to first individually delineate (IND) and then individually peer review (IPR) the contours of another participant. The contours were discussed with an expert panel consisting of two consultant clinical oncologists and a consultant radiologist. Contours were analysed quantitatively by measuring the volume and local distance standard deviation (localSD) from the reference expert consensus contour and qualitatively through visual analysis. Feedback from the participants was obtained using a questionnaire. RESULTS: All participants applied minor editing to the contours during IPR, leading to a non-statistically significant reduction in the mean delineated volume (IND = 140.92 cm3, IPR = 125.26 cm3, P = 0.211). The overall interobserver variation was similar, with a localSD of 0.33 cm and 0.38 cm for the IND and IPR, respectively (P = 0.848). Six participants (29%) carried out correct major changes by either including tumour or excluding healthy tissue. One participant (5%) carried out an incorrect edit by excluding parts of the tumour, while another observer failed to identify a major contour error. The participants' level of confidence in target volume delineation increased following the course and identified the discussions with the radiologist and colleagues as the most important highlights of the course. CONCLUSION: IPR could improve target volume delineation quality among trainee oncologists by identifying most major contour errors. However, errors were also introduced after IPR, suggesting the need to further discuss major changes with a multidisciplinary team.

Evaluation of Ultra-low-dose Paediatric Cone-beam Computed Tomography for Image-guided Radiotherapy.

AIMS: In image-guided radiotherapy, daily cone-beam computed tomography (CBCT) is rarely applied to children due to concerns over imaging dose. Simulating low-dose CBCT can aid clinical protocol design by allowing visualisation of new scan protocols in patients without delivering additional dose. This work simulated ultra-low-dose CBCT and evaluated its use for paediatric image-guided radiotherapy by assessment of image registration accuracy and visual image quality. MATERIALS AND METHODS: Ultra-low-dose CBCT was simulated by adding the appropriate amount of noise to projection images prior to reconstruction. This simulation was validated in phantoms before application to paediatric patient data. Scans from 20 patients acquired at our current clinical protocol (0.8 mGy) were simulated for a range of ultra-low doses (0.5, 0.4, 0.2 and 0.125 mGy) creating 100 scans in total. Automatic registration accuracy was assessed in all 100 scans. Inter-observer registration variation was next assessed for a subset of 40 scans (five scans at each simulated dose and 20 scans at the current clinical protocol). This subset was assessed for visual image quality by Likert scale grading of registration performance and visibility of target coverage, organs at risk, soft-tissue structures and bony anatomy. RESULTS: Simulated and acquired phantom scans were in excellent agreement. For patient scans, bony atomy registration discrepancies for ultra-low-dose scans fell within 2 mm (translation) and 1° (rotation) compared with the current clinical protocol, with excellent inter-observer agreement. Soft-tissue registration showed large discrepancies. Bone visualisation and registration performance reached over 75% acceptability (rated 'well' or 'very well') down to the lowest doses. Soft-tissue visualisation did not reach this threshold for any dose. CONCLUSION: Ultra-low-dose CBCT was accurately simulated and evaluated in patient data. Patient scans simulated down to 0.125 mGy were appropriate for bony anatomy set-up. The large dose reduction could allow for more frequent (e.g. daily) image guidance and, hence, more accurate set-up for paediatric radiotherapy.

Experimental verification the electron return effect around spherical air cavities for the MR-Linac using Monte Carlo calculation.

PURPOSE: Dose deposition around unplanned air cavities during magnetic resonance-guided radiotherapy (MRgRT) is influenced by the electron return effect (ERE). This is clinically relevant for gas forming close to or inside organs at risk (OARs) that lie in the path of a single beam, for example, intestinal track during pelvic treatment. This work aims to verify Monte Carlo calculations that predict the dosimetric effects of ERE around air cavities. For this, we use GafChromic EBT3 film inside poly-methyl methacrylate (PMMA) -air phantoms. METHOD: Four PMMA phantoms were produced. Three of the phantoms contained centrally located spherical air cavities (0.5, 3.5, 7.5 cm diameter), and one phantom contained no air. The phantoms were split to sandwich GafChromic EBT3 film in the center. The phantoms were irradiated on an Elekta Unity system using a single 10 × 10 cm2 7-MV photon beam under the influence of a 1.5-T transverse magnetic field. The measurements were replicated using the Elekta Monaco treatment planning system (TPS). Gamma analysis with pass criteria 3%/3 mm was used to compare the measured and calculated dose distributions. We also consider 3%/2 mm, 2%/3 mm, and 2%/2 mm pass criteria for interest. RESULTS: The gamma analysis showed that >95% of the points agreed between the TPS-calculated and measured dose distributions, using 3%/3 mm criteria. The phantom containing the largest air cavity had the lowest agreement, with most of the disagreeing points lying inside the air cavity (dose to air region). CONCLUSIONS: The dose effects due to ERE around spherical air cavities are being calculated in the TPS with sufficient accuracy for clinical use.

Cardiovascular mortality and morbidity following radical radiotherapy for lung cancer: Is cardiovascular death under-reported?

BACKGROUND: Lung cancer is the most common malignancy worldwide. Radical radiotherapy is an essential treatment in the management of early and locally advanced lung cancer. Cardiac events are known to occur following radical radiotherapy for lung cancer. This study examines the burden of cardiac events post radiotherapy, and estimates the accuracy of death certification in patients who received radical radiotherapy for lung cancer. METHODS: We conducted a retrospective observational cohort study for all patients receiving radical radiotherapy for non-small cell lung cancer (NSCLC) at a large cancer centre between 01/01/2010 to 31/12/2016. Baseline cardiovascular disease and cancer status and treatment data were collected, along with hospital admission data and documented cause of death from the national registry for a median follow-up period of 34 months. RESULTS: Of 1224 patients included in the analysis, 378 (30.9%) patients had cardiovascular disease at baseline, including 140 (11.4%) with prior myocardial infarction. In the 846 patients without known cardiovascular disease, 451 (53.3%) had a QRISK2 predicted 10-year cardiovascular risk >20% over 10 years. During follow-up, 215 hospitalisations occurred (Incidence rate 6.2 per hundred patient years) which were classified as primarily cardiac, and 622 patients died (18 per 100 patient-years). However, death certificates stated a primary cardiac cause of death in only 33 cases (5.3% of deaths). Notably, 29% of patients dying out of hospital and certified as cancer death did not have documented cancer relapse prior to death, and 61% had no community palliative care input prior to death, implying these events may have been sudden and unexpected. CONCLUSION: There is a high prevalence of baseline cardiovascular disease in people undergoing radiotherapy for NSCLC, accompanied by significant rates of post-radiotherapy cardiovascular hospitalisation. However, only a small proportion of deaths are attributed to cardiovascular disease, together with the large amount of sudden deaths observed, this suggests that cardiovascular death is greatly under-reported in official statistics.

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy.

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n = 142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤ 0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤ 1.7 Gy for [Formula: see text], ≤ 2.9 Gy for [Formula: see text], and ≤ 13% for [Formula: see text] and [Formula: see text], were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.

Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia.

OBJECTIVE: To compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD). BACKGROUND: Quantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans. MATERIALS AND METHODS: Manual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using test-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20  AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (NCTRL_MCI) and between controls and AD (NCTRL_AD). Finally, reproducibility of hippocampal atrophy rates was assessed using within-session rescans and expressed as an average distance measure DAve, which expresses the difference in atrophy rate, averaged over all subjects. The same DAve was used to determine the agreement between different methods. RESULTS: Except for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: NCTRL_MCI = 115, NCTRL_AD = 17 with DAve = 3.26%; MIRTK: NCTRL_MCI = 97, NCTRL_AD = 11 with DAve = 3.76%), while ANTs was most reproducible (NCTRL_MCI = 162, NCTRL_AD = 37 with DAve = 1.06%), followed by Elastix (NCTRL_MCI = 226, NCTRL_AD = 15 with DAve = 1.78%) and NiftyReg (NCTRL_MCI = 193, NCTRL_AD = 14 with DAve = 2.11%). Manually measured hippocampal atrophy rates required largest sample sizes to detect volume change and were poorly reproduced (NCTRL_MCI = 452, NCTRL_AD = 87 with DAve = 12.39%). Atrophy rates of non-linear registration methods also agreed best with each other. DISCUSSION AND CONCLUSION: Non-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.

Inter-rater agreement in glioma segmentations on longitudinal MRI.

BACKGROUND: Tumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate. METHODS: In 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement. RESULTS: For glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year. CONCLUSION: Manual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts.