Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications

The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation in 3D shapes was trained with head and neck organ segmentations from planning CT scans. We then extended the original model to incorporate imaging information using two approaches: 1) extracting features directly from image patches, and 2) including the mean square error between patches as part of the loss function. The correspondence and interpolation performance were evaluated using the geodesic error, chamfer distance and conformal distortion metrics, as well as distances between anatomical landmarks. Each of the models produced significantly better correspondences than the baseline non-rigid registration approach. The original model performed similarly to the model with direct inclusion of image features. The best performing model configuration incorporated imaging information as part of the loss function which produced more anatomically plausible correspondences. We will use the best performing model to identify corresponding anatomical points on organs to improve spatial normalisation, an important step in outcome modelling, or as an initialisation for anatomically informed registrations. All our code is publicly available at https://github.com/rrr-uom-projects/Unsup-RT-Corr-NetComment: Accepted in 3rd Workshop on Shape in Medical Imaging (ShapeMI 2023). This preprint has not undergone peer review or any post-submission improvements or correction

Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy

Breast cancer; Fibrosis; Late effectsCàncer de mama; Fibrosi; Efectes tardansCáncer de mama; Fibrosis; Efectos tardíosBackground Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis. Materials and methods 280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression. Results There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (−3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from “skin” contours showed higher agreement with observed events. Conclusion Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.REQUITE received funding from the European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 601826. We thank all patients who participated in the REQUITE study and all study personnel involved in the REQUITE project. Marianne Aznar acknowledges the support of the Engineering and Physical Sciences Research Council (Grant number EP/T028017/1) This work was supported by Cancer Research UK RadNet Manchester [C1994/A28701] and the NIHR Manchester Biomedical Research Centre (NIHR203308). The researchers at DKFZ also thank Anusha Müller, Irmgard Helmbold, Thomas Heger, Sabine Behrens, Juan Camilo Rosas. Petra Seibold was supported by ERA PerMed 2018 funding (BMBF #01KU1912) and BfS funding (#3619S42261). S. Gutiérrez-Enríquez is supported by the Government of Catalonia 2021SGR01112. The VHIO authors acknowledge the Cellex Foundation for providing research equipment and facilities and thank CERCA Program/Generalitat de Catalunya for institutional support

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Automatic Identification of Segmentation Errors for Radiotherapy Using Geometric Learning

Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to clinical use, which can be time consuming. The aim of this work was to develop a tool to automatically identify errors in 3D OAR segmentations without a ground truth. Our tool uses a novel architecture combining a CNN and graph neural network (GNN) to leverage the segmentation’s appearance and shape. The proposed model was trained using data-efficient learning using a synthetically-generated dataset of segmentations of the parotid gland with realistic contouring errors. The effectiveness of our model was assessed with ablation tests, evaluating the efficacy of different portions of the architecture as well as the use of transfer learning from a custom pretext task. Our best performing model predicted errors on the parotid gland with a precision of 85.0% &amp; 89.7% for internal and external errors respectively, and recall of 66.5% &amp; 68.6%. This offline QA tool could be used in the clinical pathway, potentially decreasing the time clinicians spend correcting contours by detecting regions which require their attention. All our code is publicly available at https://github.com/rrr-uom-projects/contour_auto_QATool

Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data

Background and purpose: Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3D auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and Method: Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors’ gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mDTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results: Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mDTA of (0.81±0.31) mm for the brainstem, (0.20±0.08) mm for the mandible, (0.77±0.14) mm for the left parotid and (0.81±0.28) mm for the right parotid. Conclusions: Through careful tuning and customisation we trained a 3D CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods

Residual setup errors caused by rotation and non-rigid motion in prone-treated cervical cancer patients after online CBCT image-guidance

Purpose: To quantify the impact of uncorrected or partially corrected pelvis rotation and spine bending on region-specific residual setup errors in prone-treated cervical cancer patients. Methods and materials: Fifteen patients received an in-room CBCT scan twice a week. CBCT scans were registered to the planning CT-scan using a pelvic clip box and considering both translations and rotations. For daily correction of the detected translational pelvis setup errors by couch shifts, residual setup errors were determined for L5, L4 and seven other points of interest (POIs). The same was done for a procedure with translational corrections and limited rotational correction (+/- 3 degree Results: With translational correction only, residual setup errors were large especially for L5/L4 in AP direction (Sigma = 5.1/5.5 mm). For the 7 POIs the residual setup errors ranged from 1.8 to 5.6 mm (AP). Using the 6D positioning device, the errors were substantially smaller (for L5/L4 in AP direction Sigma = 2.7/2.2 mm). Using this device, the percentage of fractions with a residual AP displacement for L4 > 5 mm reduced from 47% to 9%. Conclusions: Setup variations caused by pelvis rotations are large and cannot be ignored in prone treatment. of cervical cancer patients. Corrections with a 6D positioning device may considerably reduce resulting setup errors, but the residual setup errors should still be accounted for by appropriate CTV-to-PTV margins. (C) 2012 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and Oncology 103 (2012) 322-32

PANOPTES: Een nieuw hulpmiddel voor MBB'ers om kritieke organen te leren intekenen ten behoeve van radiotherapie planning

Een nauwkeurige intekening van de tumor en omliggende kritieke organen is essentieel voor radiotherapie om een zo goed mogelijk behandelresultaat te verkrijgen. Het intekenen van kritieke organen is echter gevoelig voor inter- en intra-observervariatie. Eerder onderzoek heeft uitgewezen dat de kwaliteit van intekenen van kritieke organen door MBRT studenten verbetering behoeft, maar dat hulpmiddelen hiervoor niet voorhanden zijn. In het Panoptes project is een web-based hulpmiddel voor het onderwijs ontwikkeld en getest om het intekenonderwijs te verbeteren. Het project is uitgevoerd door het Lectoraat Medische Technologie van de Hogeschool Inholland in samenwerking met de Universiteit van Manchester en het Amsterdam UMC (locatie AMC). De ontwikkelde tool is, na evaluatie, geïmplementeerd in het curriculum van de opleiding Medisch Beeldvormende en Radiotherapeutische Technieken in Haarlem