Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e., spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95±0.05, 0.85±0.04, 0.96±0.01, 0.91±0.03, 0.83±0.06 and 0.65±0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy

Clinical practice vs. state-of-the-art research and future visions:Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019

The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an informal ground for clinical medical physicists, medical physics researchers and medical doctors interested in the development of the 4D technology, protocols and their translation into clinical practice. The 10th and 11th editions of the workshop took place in Sapporo, Japan in 2018 and Krakow, Poland in 2019, respectively. This review report from the Sapporo and Krakow workshops is structured in two parts, according to the workshop programs. The first part comprises clinicians and physicists review of the status of 4D clinical implementations. Corresponding talks were given by speakers from five centers around the world: Maastro Clinic (The Netherlands), University Medical Center Groningen (The Netherlands), MD Anderson Cancer Center (United States), University of Pennsylvania (United States) and The Proton Beam Therapy Center of Hokkaido University Hospital (Japan). The second part is dedicated to novelties in 4D research, i.e. motion modelling, artificial intelligence and new technologies which are currently being investigated in the radiotherapy field

Technological Advances in the Diagnosis and Management of Pigmented Fundus Tumours

Choroidal naevi are the most common intraocular tumour. They can be pigmented or non-pigmented and have a predilection for the posterior uvea. The majority remain undetected and cause no harm but are increasingly found on routine community optometry examinations. Rarely does a naevus demonstrate growth or the onset of suspicious features to fulfil the criteria for a malignant melanoma. Because of this very small risk, optometrists commonly refer these patients to hospital eye units for a second opinion, triggering specialist examination and investigation, causing significant anxiety to patients and stretching medical resources. This PhD thesis introduces the MOLES acronym and scoring system that has been devised to categorise the risk of malignancy in choroidal melanocytic tumours according to Mushroom tumour shape, Orange pigment, Large tumour size, Enlarging tumour and Subretinal fluid. This is a simplified system that can be used without sophisticated imaging, and hence its main utility lies in the screening of patients with choroidal pigmented lesions in the community and general ophthalmology clinics. Under this system, lesions were categorised by a scoring system as ‘common naevus’, ‘low-risk naevus’, ‘high-risk naevus’ and ‘probable melanoma.’ According to the sum total of the scores, the MOLES system correlates well with ocular oncologists’ final diagnosis. The PhD thesis also describes a model of managing such lesions in a virtual pathway, showing that images of choroidal naevi evaluated remotely using a decision-making algorithm by masked non-medical graders or masked ophthalmologists is safe. This work prospectively validates a virtual naevus clinic model focusing on patient safety as the primary consideration. The idea of a virtual naevus clinic as a fast, one-stop, streamlined and comprehensive service is attractive for patients and healthcare systems, including an optimised patient experience with reduced delays and inconvenience from repeated visits. A safe, standardised model ensures homogeneous management of cases, appropriate and prompt return of care closer to home to community-based optometrists. This research work and strategies, such as the MOLES scoring system for triage, could empower community-based providers to deliver management of benign choroidal naevi without referral to specialist units. Based on the positive outcome of this prospective study and the MOLES studies, a ‘Virtual Naevus Clinic’ has been designed and adapted at Moorfields Eye Hospital (MEH) to prove its feasibility as a response to the COVID-19 pandemic, and with the purpose of reducing in-hospital patient journey times and increasing the capacity of the naevus clinics, while providing safe and efficient clinical care for patients. This PhD chapter describes the design, pathways, and operating procedures for the digitally enabled naevus clinics in Moorfields Eye Hospital, including what this service provides and how it will be delivered and supported. The author will share the current experience and future plan. Finally, the PhD thesis will cover a chapter that discusses the potential role of artificial intelligence (AI) in differentiating benign choroidal naevus from choroidal melanoma. The published clinical and imaging risk factors for malignant transformation of choroidal naevus will be reviewed in the context of how AI applied to existing ophthalmic imaging systems might be able to determine features on medical images in an automated way. The thesis will include current knowledge to date and describe potential benefits, limitations and key issues that could arise with this technology in the ophthalmic field. Regulatory concerns will be addressed with possible solutions on how AI could be implemented in clinical practice and embedded into existing imaging technology with the potential to improve patient care and the diagnostic process. The PhD will also explore the feasibility of developed automated deep learning models and investigate the performance of these models in diagnosing choroidal naevomelanocytic lesions based on medical imaging, including colour fundus and autofluorescence fundus photographs. This research aimed to determine the sensitivity and specificity of an automated deep learning algorithm used for binary classification to differentiate choroidal melanomas from choroidal naevi and prove that a differentiation concept utilising a machine learning algorithm is feasible

A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps

An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocular cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual labels are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning

Applications of advanced and dual energy computed tomography in proton therapy

This thesis focuses on advanced reconstruction methods and Dual Energy (DE) Computed Tomography (CT) applications for proton therapy, aiming at improving patient positioning and investigating approaches to deal with metal artifacts. To tackle the first goal, an algorithm for post-processing input DE images has been developed. The outputs are tumor- and bone-canceled images, which help in recognising structures in patient body. We proved that positioning error is substantially reduced using contrast enhanced images, thus suggesting the potential of such application. If positioning plays a key role in the delivery, even more important is the quality of planning CT. For that, modern CT scanners offer possibility to tackle challenging cases, like treatment of tumors close to metal implants. Possible approaches for dealing with artifacts introduced by such rods have been investigated experimentally at Paul Scherrer Institut (Switzerland), simulating several treatment plans on an anthropomorphic phantom. In particular, we examined the cases in which none, manual or Iterative Metal Artifact Reduction (iMAR) algorithm were used to correct the artifacts, using both Filtered Back Projection and Sinogram Affirmed Iterative Reconstruction as image reconstruction techniques. Moreover, direct stopping power calculation from DE images with iMAR has also been considered as alternative approach. Delivered dose measured with Gafchromic EBT3 films was compared with the one calculated in Treatment Planning System. Residual positioning errors, daily machine dependent uncertainties and film quenching have been taken into account in the analyses. Although plans with multiple fields seemed more robust than single field, results showed in general better agreement between prescribed and delivered dose when using iMAR, especially if combined with DE approach. Thus, we proved the potential of these advanced algorithms in improving dosimetry for plans in presence of metal implants

The physics of proton therapy

The physics of proton therapy has advanced considerably since it was proposed in 1946. Today analytical equations and numerical simulation methods are available to predict and characterize many aspects of proton therapy. This article reviews the basic aspects of the physics of proton therapy, including proton interaction mechanisms, proton transport calculations, the determination of dose from therapeutic and stray radiations, and shielding design. The article discusses underlying processes as well as selected practical experimental and theoretical methods. We conclude by briefly speculating on possible future areas of research of relevance to the physics of proton therapy

Multi-modal imaging in Ophthalmology: image processing methods for improving intra-ocular tumor treatment via MRI and Fundus image photography

The most common ocular tumors in the eye are retinoblastoma and uveal melanoma, affecting children and adults respectively, and spreading throughout the body if left untreated. To date, detection and treatment of such tumors rely mainly on two imaging modalities: Fundus Image Photography (Fundus) and Ultrasound (US), however, other image modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are key to confirm a possible tumor spread outside the eye cavity. Current procedures to select the best treatment and follow-up are based on manual multimodal measures taken by clinicians. These tasks often require the manual annotation and delineation of eye structures and tumors, a rather tedious and time consuming endeavour, to be performed in multiple medical sequences simultaneously. ################################ This work presents a new set of image processing methods for improving multimodal evaluation of intra-ocular tumors in 3D MRI and 2D Fundus. We first introduce a novel technique for the automatic delineation of ocular structures and tumors in the 3D MRI. To this end, we present an Active Shape Model (ASM) built out of a dataset of healthy patients to demonstrate that the segmentation of ocular structures (e.g. the lens, the vitreous humor, the cornea and the sclera) can be performed in an accurate and robust manner. To validate these findings, we introduce a set of experiments to test the model performance on eyes with presence of endophytic retinoblastoma, and discover that the segmentation of healthy eye structures is possible, regardless of the presence of the tumor inside the eyes. Moreover, we propose a specific set of Eye Patient-specific eye features that can be extracted -- Le rétinoblastome et le mélanome uvéal sont les types de cancer oculaire les plus communs, touchant les enfants et adultes respectivement, et peuvent se répandre à travers l’organisme s’ils ne sont pas traités. Actuellement, le traitement pour la détection du rétinoblastome se base essentiellement à partir de deux modalites d’imagerie fond d’œil (Fundus) et l’ultrason (US). Cependant, d’autres modalités d’imagerie comme l’Imagerie par Résonance magnétique (IRM) et la Tomodensitométrie (TDM) sont clé pour confirmer la possible expansion du cancer en dehors de la cavité oculaire. Les techniques utilisées pour déterminer la tumeur oculaire, ainsi que le choix du traitement, se basent sur des mesures multimodales réalisées de manière manuelle par des médecins. Cette méthodologie manuelle est appliquée quotidiennement et continuellement pendant toute la durée de la maladie. Ce processus nécessite souvent la délinéation manuelle des structures ocularies et de la tumeur, un mécanisme laborieux et long, effectuée dans des multiples séquences médicales simultanées (par exemple : T1-weighted et T2-weighted IRM ...) qui augmentent la difficulté pour évaluer la maladie. Le présent travail présente une nouvelle série de techniques permettant d’améliorer l´évaluation multimodale de tumeurs oculaires en IRM et Fundus. Dans un premier temps, nous intro- duisons une méthode qui assure la délinéation automatique de la structure oculaire et de la tumeur dans un IRM 3D. Pour cela, nous présentons un Active Shape Model (ASM) construite à partir d’un ensemble de données de patients en bonne santé pour prouver que la segmenta- tion automatique de la structure oculaire (par exemple : le cristallin, l´humeur aqueuse, la cornée et la sclère) peut être réalisée de manière précise et robuste. Afin de valider ces résultats, nous introduisons un ensemble d’essais pour tester la performance du modèle par rapport à des yeux de patients affectés pathologiquement par un rétinoblastome, et démontrons que la segmentation de la structure oculaire d’un œil sain est possible, indépendamment de la présence d’une tumeur à l’intérieur des yeux. De plus, nous proposons une caractérisation spécifique du patient-specific eye features qui peuvent être utile pour la segmentation de l’œil dans l’IRM 3D, fournissant des formes riches et une information importante concernant le tissu pathologique noyé dans la structure oculaire de l’œil sain. Cette information est ultérieurement utilisée pour entrainer un ensemble de classificateurs (Convolutional Neural Network (CNN), Random Forest, . . . ) qui réalise la segmentation automatique de tumeurs oculaires à l’intérieur de l’œil. En outre, nous explorons une nouvelle méthode pour évaluer des multitudes de séquences d’images de manière simultanée, fournissant aux médecins un outil pour observer l’extension de la tumeur dans le fond d’œil et l’IRM. Pour cela, nous combinons la segmentation auto- matique de l’œil de l’IRM selon la description ci-dessus et nous proposons une delineation manuelle de tumeurs oculaires dans le fond d’œil. Ensuite, nous recalons ces deux modalités d’imagerie avec une nouvelle base de points de repère et nous réalisons la fusion des deux modalités. Nous utilisons cette nouvelle méthode pour (i) améliorer la qualité de la délinéation dans l’IRM et pour (ii) utiliser la projection arrière de la tumeur pour transporter de riches me- sures volumétriques de l’IRM vers le fond d’œil, en créant une nouvelle forme 3D représentant le fond d’œil 2D dans une méthode que nous appelons Topographic Fundus Mapping. Pour tous les tests et contributions, nous validons les résultats avec une base de données d’IRM et une base de données d’images pathologiques du fond d’œil de rétinoblastome

A review of radiotherapy-induced late effects research after advanced technology treatments

The number of incident cancers and long-term cancer survivors is expected to increase substantially for at least a decade. Advanced technology radiotherapies, e.g., using beams of protons and photons, offer dosimetric advantages that theoretically yield better outcomes. In general, evidence from controlled clinical trials and epidemiology studies are lacking. To conduct these studies, new research methods and infrastructure will be needed. In the paper, we review several key research methods of relevance to late effects after advanced technology proton-beam and photon-beam radiotherapies. In particular, we focus on the determination of exposures to therapeutic and stray radiation and related uncertainties, with discussion of recent advances in exposure calculation methods, uncertainties, in silico studies, computing infrastructure, electronic medical records, and risk visualization. We identify six key areas of methodology and infrastructure that will be needed to conduct future outcome studies of radiation late effects

Development of integration mode proton imaging with a single CMOS detector for a small animal irradiation platform

A novel irradiation platform for preclinical proton therapy studies foresees proton imaging for accurate setup and treatment planning. Imaging at modern synchrocyclotron-based proton therapy centers with high instantaneous particle flux is possible with an integration mode setup. The aim of this work is to determine an object’s water-equivalent thickness (WET) with a commercially available large-area CMOS sensor. Image contrast is achieved by recording the proton energy deposition in detector pixels for several incoming beam energies (here, called probing energies) and applying a signal decomposition method that retrieves the water-equivalent thickness. A single planar 114 mm × 65 mm CMOS sensor (49.5 µm pixel pitch) was used for this study, aimed at small-animal imaging. In experimental campaigns, at two isochronous cyclotron-based facilities, probing energies suitable for small-animal-sized objects were produced once with built-in energy layer switching and the other time, using a custom degrader wheel. To assess water-equivalent thickness accuracy, a micro-CT calibration phantom with 10 inserts of tissue-mimicking materials was imaged at three phantom-to-detector distances: 3 mm, 13 mm, and 33 mm. For 3 mm and 13 mm phantom-to-detector distance, the average water-equivalent thickness error compared to the ground truth was about 1 and the spatial resolution was 0.16(3) mm and 0.47(2) mm, respectively. For the largest separation distance of 33 mm air gap, proton scattering had considerable impact and the water-equivalent thickness relative error increased to 30, and the spatial resolution was larger than 1.75 mm. We conclude that a pixelated CMOS detector with dedicated post-processing methods can enable fast proton radiographic imaging in a simple and compact setup for small-animal-sized objects with high water-equivalent thickness accuracy and spatial resolution for reasonable phantom-to-detector distances