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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. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p =.006), 0.85 (p <.001), and 0.99 (p <.001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p <.001). We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models. [ABSTRACT FROM AUTHOR]

Mise en place en 2017, la réforme du 3e cycle a modifié l'organisation de l'internat toutes spécialités confondues, et notamment l'oncologie-radiothérapie. L'internat a été scindé en 3 phases : la phase socle, la phase d'approfondissement et la phase de consolidation. Cette dernière, réalisée sous le statut de « docteur junior », créé et défini par le décret n°2018-571 du 3 juillet 2018 et l'arrêté du 16 janvier 2020, est une phase d'autonomie supervisée de l'interne. L'oncologie-radiothérapie est une spécialité singulière, aux activités multiples et complexes, et nécessite des compétences plurielles. Un guide définissant le statut du « docteur Junior » dans cette discipline apparait donc nécessaire afin de rappeler le rôle et les obligations de chaque interne. Celui-ci revêt un caractère consultatif et doit être adapté aux particularités de chaque service. Ce guide vise à aider à la mise en place de la réforme du 3e cycle en oncologie-radiothérapie, et plus particulièrement de la dernière année qu'est la phase de consolidation. Il est voué à évoluer, enrichi des retours d'expérience individuels et collectifs ainsi que du renouvellement permanent de notre spécialité. Introduced in 2017, the reform of the 3rd cycle has modified the organization of the residency in all specialties, and in particular radiation oncology. The residency was thus divided into 3 phases with increasing knowledge and responsibilities. The latter, carried out under the status of "junior doctor", created and defined by decree n°2018-571 of July 3, 2018 and the decree of January 16, 2020, is a phase of supervised autonomy of the resident. Radiotherapy is a singular specialty, with multiple and complex activities, and requires multiple skills. A guide defining the status of the "Junior Doctor" in radiation oncology therefore appears necessary, defining each resident's role and obligations. This guide is of an advisory nature and must be adapted to the particularities of each department. This guide aims to help the implementation of the reform of the 3rd cycle in radiation oncology and especially the final year called the consolidation phase. It is destined to evolve, expanded by individual and collective feedback and the constant renewal of our speciality. [ABSTRACT FROM AUTHOR]

In patients treated with radiotherapy for a locally advanced lung cancer, respect of dose constraints to organs at risks (OARs) insufficiently protects patients from acute pulmonary toxicity (APT), such toxicities being associated with a potential impact on treatment's completion and the patients' quality of life. Dosimetric planning doesn't take into account regional lung functionality. An APT prediction model combining usual dosimetry features with the mean dose (DMean Pmap) received by a voxel-based volume (Pmap) localized in the posterior right lung has been previously developed. A DMean Pmap ≥ 30.3Gy was associated with a higher risk of APT. In the present study, the authors aim to demonstrate the possibility of decreasing the DMean Pmap via a volumetric arc therapy (VMAT)-based adapted planning and evaluate the impact on the risk of APT. Among the 207 patients included in the initial study, only patients who presented with an APT ≥ grade 2 and with a probability of APT (Prob APT) ≥ 8% based on the prediction model were included. Dosimetry planning was optimized with a new constraint (DMean Pmap < 30.3Gy) added to the usual constraints. Initial and optimized treatment plans were compared using the T-test for independent variables and the non-parametric Mann-Whitney U test otherwise, regarding both doses to OARs and PTV (Planning Target Volume) coverage. Conformity and heterogeneity indexes were also compared. Risk of APT was recalculated using the new dosimetric features and the APT prediction model. Dosimetric optimization was considered successful for 27 out of the 44 included patients (61.4%), meaning the dosimetric constraint on the Pmap region was achieved without compromising the PTV coverage (p = 0.61). Optimization significantly decreased median DMean Pmap from 28.8Gy (IC95% 24.2-33.4) to 22.1Gy (IC95% 18.3-26.0). When recomputing the risk of APT using the new dosimetric features, optimization significantly reduced the risk of APT (p < 0.0001) by reclassifying 43.2% (19/44) of the patients. Our approach appears as both easily implementable on a daily basis and efficient at reducing the risk of APT. Regional radiosensitivity should be considered in usual lung dose constraints, opening the possibility of an easily implementable adaptive dosimetry planning. [ABSTRACT FROM AUTHOR]

Over 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. [ABSTRACT FROM AUTHOR]

During radiotherapy (RT) for prostate cancer (PCa), interfraction and intrafraction movements can lead to decreased target dose coverage and unnecessary over-exposure of organs at risk. New image-guided RT techniques accuracy allows planning target volume (PTV) margins reduction. We aim to assess the feasibility of a kilovoltage intrafraction monitoring (KIM) to track the prostate during RT. Between November 2017 and April 2018, 44 consecutive patients with PCa were included in an intrafraction prostate motion study using the Truebeam Auto Beam Hold® tracking system (Varian Medical Systems, United State) triggered by gold fiducials localization on kilovoltage (kV) imaging. A 5-mm PTV was considered. A significant gating event (SGE) was defined as the occurrence of an automatic beam interruption requiring patient repositioning following the detection of one fiducial outside a 5-mm target area around the marker during more than 45 seconds. Six patients could not benefit from the KIM because of technical issues (loss of one fiducial marker = 1, hip prosthesis = 4, morbid obesity causing table movements = 1). The mean rate of SGE per patient was 14 ± 19%, and the fraction average delivery time was increased by 146 ± 86 seconds. For a plan of 39 fractions of 2 Gy, the additional radiation dose increased by 0.13 ± 0.09 Gy. The mean rates of SGE were 2% and 18% (P = 0.002) in patients with planned fraction < 90 and > 90 seconds respectively, showing that duration of the session strongly interfered with prostate intrafraction movements. No other significant clinical and technical parameter was correlated with the occurrence of SGE. Automated intrafraction kV imaging can effectively perform autobeam holds due to intrafraction movement of the prostate in the large majority of patients. The additional radiation dose and delivery time are acceptable. This technique may be a cost-effective alternative to electromagnetic transponder guidance. [ABSTRACT FROM AUTHOR]