Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.

peer reviewedOver the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy

CT respiratory motion synthesis using joint supervised and adversarial learning

International audienceAbstract Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan

Kilovoltage intrafraction monitoring during normofractionated prostate cancer radiotherapy

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Utilisation de la radiomique en oncologie radiothérapie: État des lieux et challenges

International audienceRadiomics is a field that has been growing rapidly for the past ten years in medical imaging and more particularly in oncology where the primary objective is to contribute to personalised and predictive medicine. This short review aimed at providing some insights regarding the potential value of radiomics for cancer patients treated with radiotherapy. Radiomics may contribute to each stage of the patients’ management: diagnosis, planning, treatment monitoring and post-treatment follow-up (toxicity and response). However, its applicability in clinical routine is currently hindered by several factors, including lack of automation, standardisation and harmonisation. A major effort must be carried out to automate the workflow, standardise radiomics good practices and carry out large-scale studies before any transfer to daily clinical practice.La radiomique est un champ de recherche qui s’est incroyablement développé durant cette dernière décennie, en particulier en oncologie, où le but est principalement de contribuer à une médecine personnalisée et prédictive. Cette courte revue a pour but de fournir un aperçu de la valeur potentielle de la radiomique pour la prise en charge des patients atteints de cancer traités par irradiation. Le bénéfice de la radiomique se dessine en effet à chacune des étapes de la prise en charge, depuis le diagnostic jusqu’au suivi, en passant par la planification et la prédiction de la toxicité et de la réponse thérapeutique. Néanmoins, le transfert en routine clinique de la radiomique est pour l’instant ralenti par plusieurs facteurs, dont le manque d’automatisation, de standardisation et d’harmonisation. Un effort majeur doit être mené afin d’automatiser le processus, standardiser les bonnes pratiques et réaliser des études à grande échelle, avant tout transfert en routine clinique

Radiotherapy of benign intracranial tumours

International audienceWe present the updated recommendations of the French Society for Radiation Oncology on benign intracranial tumours. Most of them are meningiomas, vestibular schwannomas, pituitary adenomas, craniopharyngiomas, and glomus tumours. Some grow very slowly, and can be observed without specific treatment, especially if they are asymptomatic. Symptomatic or growing tumours are treated by surgery, which is the reference treatment. When surgery is not possible, due to the location of the lesion, or general conditions, radiotherapy can be applied, as it is if there is a postoperative growing residual tumour, or a local relapse. Indications have to be discussed at a multidisciplinary panel, with precise evaluation of the benefit and risks of the treatments. The techniques to be used are the most modern ones, as multimodal imaging and image-guided radiation therapy. Stereotactic treatments, using fractionated or single doses depending on the size or the location of the tumours, are commonly realized, to avoid as much a possible the occurrence of late side effects

Radiotherapy in the management of lung oligometastases

International audienceIn recent years, the development of both medical imaging and new systemic agents (targeted therapy and immunotherapy) have revolutionized the field of oncology, leading to a new entity: oligometastatic disease. Adding local treatment of oligometastases to systemic treatment could lead to prolonged survival with no significant impact on quality of life. Given the high prevalence of lung oligometastases and the new systemic agents coming with increased pulmonary toxicity, this article provides a comprehensive review of the current state-of-art for radiotherapy of lung oligometastases. After reviewing pretreatment workup, the authors define several radiotherapy regimen based on the localization and size of the oligometastases. A comment on the synergistic combination of medical treatment and radiotherapy is also made, projecting on future steps in this specific clinical setting