Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Vérification de Modèle Probabiliste pour la Reconnaissance d'Activité Humaine dans les Jeux Sérieux Médicaux.

International audienceHuman activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. Starting from an activity description enriched with event occurrence probabilities, we translate it into a corresponding formal model based on discrete-time Markov chains (DTMCs). We use the PRISM framework and its model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach with the models of several serious games used by clinicians to monitor Alzheimer patients. We expect that such a modeling approach could provide new indications for interpreting patient performances. This paper addresses the definition of patient's models for three serious games and the suitability of this approach to check behavioral properties of medical interest. Indeed, this is a mandatory first step before clinical studies with patients playing these games. Our goal is to provide a new tool for doctors to evaluate patients.La reconnaissance d'activités humaines joue un rôle important en particulier dans les applications médicales. Cet article propose une approche formelle pour modéliser ces activités en prenant en compte les variations possibles du comportement humain. À partir d'une description de l'activité dans laquelle les probabilité associées aux évènements sont données, nous conceptualisons un modèle formel fondé sur les chaînes de Markov à temps discret. Nous utilisons l'environnement PRISM avec son système de vérification de modèles pour exprimer en logique temporelle et vérifier des propriétés intéressantes concernant l'évolution dynamique des activités. Nous illustrons notre approche avec plusieurs jeux sérieux utilisés par les cliniciens pour évaluer les patients Alzheimer. Nous espérons que cette approche de modélisation apporte de nouvelles indications dans l'interprétation des performances des patients. Cet article se concentre sur la définition des modèles de patients pour trois jeux sérieux ainsi que sur la pertinence d'une telle approche formelle pour vérifier des propriétés sur les comportements de patients ayant un intérêt médical. Il s'agit d'une étape préliminaire obligatoire avant de passer à une étude clinique où de vrais patients joueront à ces jeux. Notre but est de proposer un nouvel outil aux médecins pour évaluer leurs patients