Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy

International audiencePlanning of radiotherapy involves accurate segmentation of a large number of organs at risk (OAR), i.e., organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of OAR inside the head, from magnetic resonance images (MRIs). Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem, and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step. In particular, we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered OAR, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs were evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 to 0.7 mm across different organs. A vast majority (96%) of the produced segmentations were found acceptable for radiotherapy planning

Brain Tumor Growth Simulation

In the present report, we propose a new model to simulate the growth of glioblastomas multiforma (GBM), the most aggressive glial tumors. Because the GBM shows a preferential growth in the white fibers and have a distinct invasion speed with respect to the nature of the invaded tissue, we rely on an anatomical atlas to introduce this information into the model. This atlas includes a white fibers diffusion tensor information and the delineation of cerebral structures having a distinct response to the tumor aggression. We use the finite element method (FEM) to simulate both the invasion of the GBM in the brain parenchyma and its mechanical interaction (mass effect) with the invaded structures. The former effect is modeled with either a reaction-diffusion or a Gompertz equation depending on the considered tissue, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. This tumor growth model is assessed by comparing the \textit{in-silico} GBM growth with the real GBM growth observed between two magnetic resonance images (MRIs) of a patient acquired with six months difference. The quality of the results shows the feasibility of modeling the complex behavior of brain tumors and will justify a further validation of this new conceptual approach

Early Toxicities After High Dose Rate Proton Therapy in Cancer Treatments

Background: The conventional dose rate of radiation therapy is 0.01-0.05 Gy per second. According to preclinical studies, an increased dose rate may offer similar anti-tumoral effect while dramatically improving normal tissue protection. This study aims at evaluating the early toxicities for patients irradiated with high dose rate pulsed proton therapy (PT). Materials and methods: A single institution retrospective chart review was performed for patients treated with high dose rate (10 Gy per second) pulsed proton therapy, from September 2016 to April 2020. This included both benign and malignant tumors with ≥3 months follow-up, evaluated for acute (≤2 months) and subacute (>2 months) toxicity after the completion of PT. Results: There were 127 patients identified, with a median follow up of 14.8 months (3-42.9 months). The median age was 55 years (1.6-89). The cohort most commonly consisted of benign disease (55.1%), cranial targets (95.1%), and were treated with surgery prior to PT (56.7%). There was a median total PT dose of 56 Gy (30-74 Gy), dose per fraction of 2 Gy (1-3 Gy), and CTV size of 47.6 ml (5.6-2,106.1 ml). Maximum acute grade ≥2 toxicity were observed in 49 (38.6%) patients, of which 8 (6.3%) experienced grade 3 toxicity. No acute grade 4 or 5 toxicity was observed. Maximum subacute grade 2, 3, and 4 toxicity were discovered in 25 (19.7%), 12 (9.4%), and 1 (0.8%) patient(s), respectively. Conclusion: In this cohort, utilizing high dose rate proton therapy (10 Gy per second) did not result in a major decrease in acute and subacute toxicity. Longer follow-up and comparative studies with conventional dose rate are required to evaluate whether this approach offers a toxicity benefit

Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth using a Mechanically-coupled Reaction-Diffusion Model

Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal evolution was simulated using the Finite-Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter fiber tracks is not a major determinant of tumor shape in the setting of reaction-diffusion based tumor growth models and for most locations across the brain

Mise en œuvre et évaluation d'outils de fusion d'image en radiothérapie

Le fichier contient l'intégralité de la thèse en français et en anglaisCancer is a major problem of public health. Treatment can be done in a general or loco-regional wa, in this last case medical images are important as they specify the localization of the tumour. The objective of the radiotherapy is to deliver a curative dose of radiation in the target volume while sparing the organs at risks (OAR). The determination of the accurate localization of the targets volume as well as OAR make it possible to define the balistic of irradiation beams. After the description of the principles of radiotherapy and cancers treatment, we specify the clinical stakes of ocular, cerebral and prostatic tumours.We present a state of the art of image matching, the various techniques reviewed with an aim of being didactic with respect to the medical community. The results of matching are presented within the framework of the planning of the cerebral and prostatic radiotherapy in order to specify the types of applicable matching in oncology and more particularly in radiotherapy. Then, we present the prospects for this type of application according to various anatomical areas. Applications of automatic segmentation and the evaluation of the results in the framework of brain tumour are described after a review of the various segmentation methods according to anatomical localizations.We will see an original application : the digital simulation of the virtual tumoral growth and the comparison with the real growth of a cerebral tumour presented by a patient.Lastly, we will expose the future developments possible of the tools for image processing in radiotherapy as well as the tracks of research to be explored in oncologyLe cancer est un problème majeur de santé publique. Les traitements peuvent être à visée systémique ou loco-régionale, dans ce dernier cas l'imagerie médicale joue un rôle important en permettant de préciser la localisation de la tumeur. L'objectif de la radiothérapie est de délivrer une dose curatrice de radiation au volume cible tout en épargnant les organes à risques (OAR) avoisinants. La détermination de la localisation précise de ce volume cible ainsi que des OAR permet de définir la position et la puissance des faisceaux d'irradiation. Après le rappel des principes de la radiothérapie et du traitement des cancers, nous précisons les enjeux cliniques des tumeurs oculaires, cérébrales et prostatiques.Nous effectuerons une mise au point sur le recalage d'images, les différentes techniques sont passées en revue dans le but d'être didactique vis-à-vis de la communauté médicale. Les résultats du recalage sont présentés dans le cadre de la planification de la radiothérapie cérébrale et prostatique afin de préciser les types de recalage applicables en oncologie et plus particulièrement à la radiothérapie.Ensuite, nous présentons les perspectives de ce type d'application selon différentes régions anatomiques. Les applications de la segmentation automatiques et l'évaluation des résultats dans le cadre des tumeur de l'encéphale sont décrits après avoir passé en revue les différentes méthodes de segmentation utilisables selon les localisations anatomiques.Nous verrons une application originale : la simulation numérique de la croissance tumorale virtuelle et la comparaison avec la croissance réelle d'une tumeur cérébrale présentée par un patient.Enfin, nous exposerons les futurs développements possibles des outils de traitement de l'image en radiothérapie ainsi que les pistes des recherche à explorer en oncologie

Indications et résultats de la radiothérapie stéréotaxique robotisée par le Cyberknife de Nice (étude prospective 2007)

NICE-BU Médecine Odontologie (060882102) / SudocSudocFranceF

Mise en oeuvre et évaluation d'outils de fusion d'image en radiothérapie

Le cancer est un problème majeur de santé publique. Les traitements peuvent être à visée systémique ou loco-régionale, dans ce dernier cas l imagerie médicale jour un rôle important, permettant de préciser la localisation de la tumeur. L objectif de la radiothérapie est de délivrer une dose curatrice de radiation au volume cible tout en épargnant les organes à risques (OAR) avoisinants. La détermination de la localisation précise de ce volume cible ainsi que des OAR permet de définir la balistique des faisceaux d irradiation. Après le rappel des principes de la radiothérapie, nous précisons les enjeux cliniques des tumeurs oculaires, cérébrales et prostatiques. Les différentes techniques de recalage d images sont passées en revue dans le but d être didactique vis-à-vis de la communauté médicale. Les résultats du recalage sont présentés dans le cadre de la planification de la radiothérapie oculaire, cérébrale et prostatique afin de préciser les types de recalage applicable en oncologie. Nous présentons les perspectives de ce type d application selon différentes régions anatomiques ainsi que son intérêt dans la segmentation automatique. Les applications de la segmentation automatique et l évaluation des résultats dans le cadre des tumeurs de l encéphale sont décrits après avoir passé en revue les différentes méthodes de segmentation selon les localisations anatomiques. Une application originale est la simulation numérique de la croissance tumorale virtuelle qui est comparée avec la croissance réelle d une tumeur cérébrale présentée par un patient. Nous concluons avec les différents développements de la fusion d image ainsi que les pistes de recherche devant être explorées.Cancer is a major problem of public health. Treatment can be done in a general or loco-regional way, in which case medical images are important as they specify the localization of the tumour. The objective of the radiotherapy is to deliver a curative dose of radiation in the target volume while sparing the organs at risks (OAR). The determination of the accurate localization of the targets volume as well as OAF makes it possible to define the ballistics of irradiation beams. After the description of the principles of radiotherapy and cancers treatment, we specify the clinical stakes of ocular, cerebral and prostatic tumours. We review the state of the art image matching algorithms, with a didactic purpose for the medical community. The results of image matching techniques are presented in the framework of cerebral and prostatic radiotherapy planning in order to determine the types of applicable method in oncology. Then, we present the prospects for these methods with respect to the anatomical localization and automatic segmentation. Applications of automatic segmentation and the evaluation of the results in the framework of brain tumour are described after a review of the various segmentation methods according to anatomical localizations. An original application is the digital simulation of the virtual tumoral growth and the comparison with the real growth of a cerebral tumour presented by a patient. We conclude with the future developments possible of the tools for image processing in radiotherapy as well as the tracks of research to be explored in oncology.NICE-BU Sciences (060882101) / SudocSOPHIA ANTIPOLIS-INRIA I3S (061522305) / SudocSudocFranceF

Radiothérapie stéréotaxique ablative par cyberknife dans les carcinomes bronchiques non à petites cellules localement avancés, en phase initiale et en réirradiation après rechute locale

NICE-BU Médecine Odontologie (060882102) / SudocSudocFranceF

Projet de reconstruction tridimensionnelle de l'oeil à partir des images scanneurs, IRM et de fundographie. Utilisation pour la protonthérapie des tumeurs oculaires

National audienceno abstrac