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Background Intra-individual heterogeneity of cardiac exposure is an issue in breast cancer (BC) radiotherapy that was poorly considered in previous cardiotoxicity studies mainly based on mean heart dose (MHD). This dosimetric study analyzes the distribution of individually-determined radiation doses to the heart and its substructures including coronary arteries and evaluate whether MHD is a relevant surrogate parameter of dose for these substructures. Methods Data were collected from the BACCARAT prospective study that included left or right unilateral BC patients treated with 3D-Conformal Radiotherapy (RT) between 2015 and 2017 and followed-up for 2 years with repeated cardiac imaging examinations. A coronary computed tomography angiography (CCTA) was performed before RT for all patients. Registration of the planning CT and CCTA images allowed delineation of the coronary arteries on the planning CT images. Using the 3D dose matrix generated during treatment planning and the added coronary contours, dose distributions were generated for whole heart and the following substructures: left ventricle (LV), left main coronary artery (LMCA), left anterior descending artery (LAD), left circumflex artery (LCX) and right coronary artery (RCA). A descriptive analysis of the physical doses in Gray (Gy) was performed, Dmean was the volume-weighted mean dose. Results Dose distributions were generated for 89 left-sided BC patients (MHD = 2.9 ± 1.5 Gy, Dmean_LAD = 15.7 ± 3.1 Gy) and 15 right-sided BC patients (MHD = 0.5 ± 0.1 Gy; Dmean_RCA = 1.2 ± 0.4 Gy). For left-sided BC patients, the ratio Dmean_LAD/MHD was around 5. Pearson correlation coefficients between MHD and Dmean for delineated substructures were all statistically significant. However, for all substructures, the coefficient of determination R 2 indicated that the proportion of the variance in Dmean of the substructure predictable from MHD was moderate to low (in particular R 2  = 0.45 for LAD). Among left-sided BC patients with MHD < 3Gy, 56% of patients could nevertheless receive LAD doses above 40Gy (V40 > 0). Conclusion Our study illustrates that MHD is not enough to predict with confidence individual patient dose to the LV and coronary arteries, in particular the LAD. For precise radiotherapy-induced cardiotoxicity studies it would be necessary to consider the distribution of doses within these cardiac substructures rather than just the MHD. Trial registration ClinicalTrials.gov: NCT02605512 , Registered 6 November 2015 - Retrospectively registered.

Background Breast cancer (BC) radiotherapy (RT) can induce cardiotoxicity, with adverse events often observed many years after BC RT. Subclinical left ventricular (LV) dysfunction can be detected early after BC RT with global longitudinal strain (GLS) measurement based on 2D speckle-tracking echocardiography. This 6-month follow-up analysis from the BACCARAT prospective study aimed to investigate the association between cardiac radiation doses and subclinical LV dysfunction based on GLS reduction. Methods The patient study group consisted of 79 BC patients (64 left-sided BC, 15 right-sided BC) treated with RT without chemotherapy. Echocardiographic parameters, including GLS, were measured before RT and 6 months post-RT. The association between subclinical LV dysfunction, defined as GLS reduction > 10%, and radiation doses to whole heart and the LV were performed based on logistic regressions. Non-radiation factors associated with subclinical LV dysfunction including age, BMI, hypertension, hypercholesterolemia and endocrine therapy were considered for multivariate analyses. Results A mean decrease of 6% in GLS was observed (− 15.1% ± 3.2% at 6 months vs. − 16.1% ± 2.7% before RT, p  = 0.01). For left-sided patients, mean heart and LV doses were 3.1 ± 1.3 Gy and 6.7 ± 3.4 Gy respectively. For right-sided patients, mean heart dose was 0.7 ± 0.5 Gy and median LV dose was 0.1 Gy. Associations between GLS reduction > 10% (37 patients) and mean doses to the heart and the LV as well as the V20 were observed in univariate analysis (Odds Ratio = 1.37[1.01–1.86], p  = 0.04 for Dmean Heart; OR = 1.14 [1.01–1.28], p  = 0.03 for Dmean LV; OR = 1.08 [1.01–1.14], p  = 0.02 for LV V20). In multivariate analysis, these associations did not remain significant after adjustment for non-radiation factors. Further exploratory analysis allowed identifying a subgroup of patients (LV V20 > 15%) for whom a significant association with subclinical LV dysfunction was found (adjusted OR = 3.97 [1.01–15.70], p  = 0.048). Conclusions This analysis indicated that subclinical LV dysfunction defined as a GLS decrease > 10% is associated with cardiac doses, but adjustment for non-radiation factors such as endocrine therapy lead to no longer statistically significant relationships. However, LV dosimetry may be promising to identify high-risk subpopulations. Larger and longer follow-up studies are required to further investigate these associations. Trial registration ClinicalTrials.gov: NCT02605512 , Registered 6 November 2015 - Retrospectively registered

Background Radioresistant glioblastoma stem cells (GSCs) contribute to tumor recurrence and identification of the molecular targets involved in radioresistance mechanisms is likely to enhance therapeutic efficacy. This study analyzed the DNA damage response following ionizing radiation (IR) in 10 GSC lines derived from patients. Methods DNA damage was quantified by Comet assay and DNA repair effectors were assessed by Low Density Array. The effect of RAD51 inhibitor, RI-1, was evaluated by comet and annexin V assays. Results While all GSC lines displayed efficient DNA repair machinery following ionizing radiation, our results demonstrated heterogeneous responses within two distinct groups showing different intrinsic radioresistance, up to 4Gy for group 1 and up to 8Gy for group 2. Radioresistant cell group 2 (comprising 5 out of 10 GSCs) showed significantly higher RAD51 expression after IR. In these cells, inhibition of RAD51 prevented DNA repair up to 180 min after IR and induced apoptosis. In addition, RAD51 protein expression in glioblastoma seems to be associated with poor progression-free survival. Conclusion These results underscore the importance of RAD51 in radioresistance of GSCs. RAD51 inhibition could be a therapeutic strategy helping to treat a significant number of glioblastoma, in combination with radiotherapy.

Background Catheter ablation is a key treatment for atrial fibrillation (AF). This procedure is clearly identifiable in French medical‐administrative databases and can be used as a surrogate for symptomatic patients with drug‐refractory or symptomatic paroxysmal AF forms. Breast cancer (BC) patients have an increased risk of AF, but knowledge about AF forms treated by ablation is limited. Aims Based on a representative cohort of BC patients from the French National Health Databases, we aimed to assess the occurrence of AF catheter ablation in these patients, compared to the general population. Methods Patients with a first diagnosis of BC between 2008 and 2018 and followed for at least 1 year until 2019 were eligible. The incidence rates of AF ablation among BC patients were compared to those in the general population by estimating a standardized incidence ratio (SIR). A competing risk survival model was used to evaluate the occurrence of AF ablation in BC patients treated with or without radiotherapy (RT) (subdistribution Hazard Ratio—sdHR). Results We included 3667 patients (78% with RT). Overall, 16 cases underwent AF ablation, whereas 8.3 cases were expected in the general population, resulting in a significant SIR of 1.93 [1.10–3.00] (p < 0.05). After a mean follow‐up of 6.6 years, the cumulative incidence of AF ablation at 5 years was 0.14% [0.05–0.35] in the RT group and 0.47% [0.13–1.31] in the non‐RT group, with no significant difference in the age‐adjusted survival analysis (sdHR = 0.65 [0.21–2.01]). Conclusions Our exploratory study revealed that BC patients had a twofold greater rate of first AF ablation than the age‐comparable general population, without a link with RT. These results should be interpreted cautiously because of the limited size of the study population and the differing characteristics between the RT and non‐RT groups.

Atrial fibrillation (AF) after radiotherapy (RT) in patients with breast cancer (BC) is a relatively new and understudied topic. AF can increase the risk of stroke and other serious cardiovascular complications, compromising patients' quality of life and survival. Screening of AF, both asymptomatic and symptomatic forms, is therefore essential for optimal management. The aim of the Watch Your Heart After Radiotherapy for Breast Cancer (WATCH) study is to assess the incidence of AF (symptomatic or asymptomatic) occurring throughout a 5-year follow-up after RT and to investigate whether cardiac radiation exposure is associated with the occurrence of such events. WATCH is a cohort study that will include 200 patients over 65 years old, treated with RT for BC 5 years before inclusion and without a history of AF. Cross-sectional screening for AF at the time of the scheduled 5-year post-RT visit will be conducted by recording data from a Withings ScanWatch smartwatch for 1 month, confirmed by an electrocardiogram (ECG), and validated by a physician. In addition, a transthoracic echocardiography (TTE) will be performed, providing a comprehensive assessment of cardiac structures, and allowing us to investigate the underlying etiology and assess the risk of complications. Patients' medical records will provide retrospective information about the timing and risk factors for the occurrence of AF and other arrhythmias and cardiac diseases during the 5 years following RT. The development of deep learning algorithms for autosegmentation analysis of potentially critical substructures for the occurrence of AF, including cardiac chambers, the sinoatrial node, the atrioventricular node, coronary arteries, and pulmonary veins (PVs), will produce dosimetry linked to previous RT treatment for all contoured structures. Enrollment started in October 2023 and will continue until mid-2026 to include 200 patients, which will ensure an 80% statistical power to detect a significant difference in AF incidence around 15% for the group of patients moderately exposed (<75th percentile of the mean heart radiation dose) and 25% for the group of patients highly exposed (>75th percentile of the mean heart radiation dose). The results are expected by the end of 2026. This study will contribute to generating new knowledge on AF after RT for BC and help considering the inclusion of AF screening into routine clinical practice for these patients. Identifying the dose-risk associations would improve RT delivery protocols to limit the occurrence of different forms of AF and, if necessary, initiate appropriate treatment. ClinicalTrials.gov NCT06073509; clinicaltrials.gov/study/NCT06073509?id=NCT06073509&rank=1. DERR1-10.2196/67875.

The severity of breakthrough cancer pain (BTcP) impacts patients' quality of life, increases the risk of anxiety and depression, lowers functional capacities, and may lead to poor compliance with cancer treatments. The aim of the current study was to assess, in a real-life setting, patient satisfaction with a fentanyl-pectin-nasal-spray (FPNS) for BTcP management in head and neck (H&N) cancer patients treated by radiotherapy. This non-interventional, prospective study was conducted in 92 adult H&N-cancer patients undergoing radiotherapy and who started FPNS treatment for BTcP. Throughout the radiotherapy period, the patients completed self-diaries to assess their BTcP episodes, FPNS use, satisfaction on FPNS efficiency (primary outcome), tolerability and ease of use. Prior to FPNS treatment, 86% of the patients were experiencing ≤4 BTcP episodes/day. During the radiotherapy period, the BTcP episodes were treated with a median dose of 100µg of FPNS. Patients were \"satisfied/very-satisfied\" with the efficiency (73% of assessments), ease of use (87% of assessments) and tolerability (87% of assessments) of FPNS. In total, 27% of patients reported at least one adverse event related to FPNS and 4% of patients discontinued treatment due to adverse events. None of the adverse events were serious. Patient quality of life was maintained throughout the radiotherapy period. This study showed, in a real-life setting, that a clear majority of H&N cancer patients treated with FPNS for BTcP throughout radiotherapy expressed satisfaction with this analgesic treatment.

A better understanding of the relationship between glioblastomas molecular subtypes and radio-chemotherapy is needed for the development of individualized strategies. In this study, we aimed to assess whether non-homologous end-joining (NHEJ) protein expression is associated and could predict responses to treatment of mesenchymal (MES) and proneural (PN) subtypes. Tumors from 122 patients with a glioblastoma treated at the University Hospital of Poitiers between 2002–2013 by an association of radiotherapy and temozolomide were collected. Among these tumors, 80 were suitable for in situ analysis and were included in TissueMicroArray. The expression of DNA-PKcs, Ku70, Ku80 and CD44, Olig2 (respectively surrogate markers of MES and PN subtypes) were evaluated by immunohistochemistry. The median survival of patients with high and low CD44 expression was 11.9 months (95% CI 7.7–14) and 19.1 months (95% CI 15.2–22.4) respectively (p = 0.008). Median survival of patients with high and low DNA-PKcs levels was 20.0 months (95% CI 15.2–25.3) and 12.9 months (95% CI 9.9–19.5) respectively (p = 0.036). High levels of Olig2, Ku70 and Ku80 tended to be associated with better overall survival but no significant differences were found. Overall survival of class I patients (CD44+ and DNA-PKcs+) was longer than class II (CD44+ and DNA-PKcs− or CD44− and DNA-PKcs+) and class III (CD44− and DNA-PKcs−), (p = 0.005 and 0.003 respectively). High levels of CD44 and DNA-PK are associated with a better survival and better response to radiotherapy and temozolomide and could establish prognosis classes by predicting survival and response to therapy for GBMs patients.

The aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies.

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.