Image guidance and inter-fractional anatomical variation in paediatric abdominal radiotherapy

OBJECTIVES: To identify variables predicting inter fractional anatomical variationsmeasured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes. METHODS: Metrics of variation in gastrointestinal (GI) gas volume andseparation of the body contour and abdominal wallwere calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4y, range: 2 -19y). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore,GI gas variationwas correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotationalcorrections between CT/CBCT. RESULTS: GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separationvaried 2.0 ± 0.7 mm and4.1±1.5mmfrom planning, respectively. Patients < 3.5y (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anteroposterior translation (R = 0.65) androtation of the left-right axis (R = -0.36). CONCLUSIONS: Young age, GA, and absence of feeding tubes were linked to stronger inter fractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways.Our data suggests a role for SGRT toinformthe need for CBCT at each treatment fractionin this patient group. ADVANCES IN KNOWLEDGE: This is the first study to suggest the potential role of SGRT for the management of internal inter fractional anatomical variation in paediatric abdominal radiotherapy

Developing a framework for CBCT-to-CT synthesis in paediatric abdominal radiotherapy

We proposed a CBCT-to-CT synthesis framework tailored for paediatric abdominal patients. Our approach was based on the cycle-consistent generative adversarial network (cycleGAN) modified to preserve structural consistency. To adjust for differences in field-of-view and body size from different patient groups, our training data was spatially co-registered to a common field-of-view and normalised to a fixed size. The proposed framework showed improvements in generating synthetic CTs from CBCTs compared to the original implementation of cycleGAN without field-of-view adjustments and structural consistency constrain

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective: Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve quality of cone beam CT (CBCT) images for dose calculation using deep learning. / Approach: We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative 10 Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and smaller patient numbers. We introduced the concept of global residuals only learning to the networks and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the 15 paediatric population, we applied a smart 2D slice selection based on the common field-of-view across the dataset (abdomen). This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen 20 dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics. / Main results: We found improved performance, compared to a baseline implementation, on imagesimilarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0±16.6 proposed vs 58.9±16.8 baseline). There was also a higher level of structural agreement for gastrointestinal gas 25 between source and synthetic images measured through dice similarity overlap (0.872±0.053 proposed vs 0.846±0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3±2.4% proposed vs 3.7±2.8% baseline). / Significance: Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated

Common genetic variants in the CLDN2 and PRSS1-PRSS2 loci alter risk for alcohol-related and sporadic pancreatitis

Pancreatitis is a complex, progressively destructive inflammatory disorder. Alcohol was long thought to be the primary causative agent, but genetic contributions have been of interest since the discovery that rare PRSS1, CFTR, and SPINK1 variants were associated with pancreatitis risk. We now report two significant genome-wide associations identified and replicated at PRSS1-PRSS2 (1×10-12) and x-linked CLDN2 (p < 1×10-21) through a two-stage genome-wide study (Stage 1, 676 cases and 4507 controls; Stage 2, 910 cases and 4170 controls). The PRSS1 variant affects susceptibility by altering expression of the primary trypsinogen gene. The CLDN2 risk allele is associated with atypical localization of claudin-2 in pancreatic acinar cells. The homozygous (or hemizygous male) CLDN2 genotype confers the greatest risk, and its alleles interact with alcohol consumption to amplify risk. These results could partially explain the high frequency of alcohol-related pancreatitis in men – male hemizygous frequency is 0.26, female homozygote is 0.07

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning