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Purpose: This study aimed to evaluate the intrafractional prostate motion captured during gated magnetic resonance imaging (MRI)-guided online adaptive radiotherapy for prostate cancer and analyze its impact on the delivered dose as well as the effect of gating. Methods: Sagittal 2D cine-MRI scans were acquired at 4 Hz during treatment at a ViewRay MRIdian (ViewRay Inc., Oakwood Village, OH, USA) MR linac. Prostate shifts in anterior–posterior (AP) and superior–inferior (SI) directions were extracted separately. Using the static dose cloud approximation, the planned fractional dose was shifted according to the 2D gated motion (residual motion in gating window) to estimate the delivered dose by superimposing and averaging the shifted dose volumes. The dose of a hypothetical non-gated delivery was reconstructed similarly using the non-gated motion. For the clinical target volume (CTV), rectum, and bladder, dose–volume histogram parameters of the planned and reconstructed doses were compared. Results: In total, 174 fractions (15.7 h of cine-MRI) from 10 patients were evaluated. The average (±1 σ) non-gated prostate motion was 0.6 ± 1.0 mm in the AP and 0.0 ± 0.6 mm in the SI direction with respect to the centroid position of the gating boundary. 95% of the shifts were within [−3.5, 2.7] mm in the AP and [−2.9, 3.2] mm in the SI direction. For the gated treatment and averaged over all fractions, CTV D98% decreased by less than 2% for all patients. The rectum and the bladder D2% increased by less than 3% and 0.5%, respectively. Doses reconstructed for gated and non-gated delivery were similar for most fractions. Conclusion: A pipeline for extraction of prostate motion during gated MRI-guided radiotherapy based on 2D cine-MRI was implemented. The 2D motion data enabled an approximate estimation of the delivered dose. For the majority of fractions, the benefit of gating was negligible, and clinical dosimetric constraints were met, indicating safety of the currently adopted gated MRI-guided treatment workflow. [ABSTRACT FROM AUTHOR]

Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation [Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80] in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation. [ABSTRACT FROM AUTHOR]

A novel strategy for 4D PET optimization in the sinogram domain is proposed, aiming at motion model application before image reconstruction ("sinogram warping" strategy). Compared to state-of-the-art 4D-MLEM reconstruction, the proposed strategy is able to optimize the image SNR, avoiding iterative direct and inverse warping procedures, which are typical of the 4D-MLEM algorithm. A full-count statistics sinogram of the motion-compensated 4D PET reference phase is generated by warping the sinograms corresponding to the different PET phases. This is achieved relying on a motion model expressed in the sinogram domain. The strategy was tested on the anthropomorphic 4D PET-CT NCAT phantom in comparison with the 4D-MLEM algorithm, with particular reference to robustness to PET-CT co-registrations artefacts. The MLEM reconstruction of the warped sinogram according to the proposed strategy exhibited better accuracy (up to +40.90 % with respect to the ideal value), whereas images reconstructed according to the 4D-MLEM reconstruction resulted in less noisy (down to -26.90 % with respect to the ideal value) but more blurred. The sinogram warping strategy demonstrates advantages with respect to 4D-MLEM algorithm. These advantages are paid back by introducing approximation of the deformation field, and further efforts are required to mitigate the impact of such an approximation in clinical 4D PET reconstruction. [ABSTRACT FROM AUTHOR]

Increasing evidences suggest that mitochondrial dysfunction plays an important role in the pathogenesis of neurodegenerative diseases including Alzheimer's disease (AD). Alterations of mitochondrial efficiency and function are mainly related to alterations in mitochondrial content, amount of respiratory enzymes, or changes in enzyme activities leading to oxidative stress, mitochondrial permeability transition pore opening, and enhanced apoptosis. More recently, structural changes of the network are related to bioenergetic function, and its consequences are a matter of intensive research. Several mitochondria-targeting compounds with potential efficacy in AD including dimebon, methylene blue, piracetam, simvastatin, Ginkgo biloba, curcumin, and omega-3 polyunsaturated fatty acids have been identified. The majority of preclinical data indicate beneficial effects, whereas most controlled clinical trials did not meet the expectations. Since mitochondrial dysfunction represents an early event in disease progression, one reason for the disappointing clinical results could be that pharmacological interventions might came too late. Thus, more studies are needed that focus on therapeutic strategies starting before severe disease progress. [ABSTRACT FROM AUTHOR]

As a fully differentiated organ, our brain is very sensitive to cumulative oxidative damage of proteins, lipids, and DNA occurring during normal aging because of its high energy metabolism and the relative low activity of antioxidative defense mechanisms. As a major consequence, perturbations of energy metabolism including mitochondrial dysfunction, alterations of signaling mechanisms and of gene expression culminate in functional deficits. With the increasing average life span of humans, age-related cognitive disorders such as Alzheimer’s disease (AD) are a major health concern in our society. Age-related mitochondrial dysfunction underlies most neurodegenerative diseases, where it is potentiated by disease-specific factors. AD is characterized by two major histopathological hallmarks, initially intracellular and with the progression of the disease extracellular accumulation of oligomeric and fibrillar β-amyloid peptides and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. In this review, we focus on findings in AD animal and cell models indicating that these histopathological alterations induce functional deficits of the respiratory chain complexes and therefore consecutively result in mitochondrial dysfunction and oxidative stress. These parameters lead synergistically with the alterations of the brain aging process to typical signs of neurodegeneration in the later state of the disease, including synaptic dysfunction, loss of synapses and neurites, and finally neuronal loss. We suggest that mitochondrial protection and subsequent reduction of oxidative stress are important targets for prevention and long-term treatment of early stages of AD. [ABSTRACT FROM AUTHOR]