It has been established that scar acellular matrices (AMs), which allow cell proliferation, have similar characteristics. The aim of this study was to investigate the repair effect of scar AMs on animals, thus providing a reference for clinical application. Selected mature and immature scar AMs were implanted into animals, and then a negative control group was set for comparison. The effect of scar AMs on wound healing was observed through tissue staining, RT-qPCR, and immunohistochemistry. The materials showed milder inflammation and faster extracellular matrix (ECM) deposition than the negative control group. The ECM deposition and new vessels increased over time. However, the arrangement of ECM in mature scar AM was more regular than in immature scar AM and the negative control group, and more new vessels grew in the mature scar AM group than in the immature scar AM group and negative control group over the same period. The transforming growth factor-β level was elevated at one month, two months, and six months. COLA1 and vimentin levels all peaked at six months. Matrix metalloproteinase and TIMP1 were also elevated at different months. Collectively, scar AMs can effectively promote wound healing and vascularization. Mature scar AMs have a better regeneration effect.Transarterial radioembolization (TARE) with 90Y-loaded microspheres is an established therapeutic option for inoperable hepatic tumors. Increasing knowledge regarding TARE hepatic dose-response and dose-toxicity correlation is available but few studies have investigated dose-toxicity correlation in extra-hepatic tissues. We investigated absorbed dose levels for the appearance of focal lung damage in a case of off-target deposition of 90Y microspheres and compared them with the corresponding thresholds recommended to avoiding radiation induced lung injury following TARE. A 64-year-old male patient received 1.6 GBq of 90Y-labelled glass microspheres for an inoperable left lobe hepatocellular carcinoma. A focal off-target accumulation of radiolabeled microspheres was detected in the left lung upper lobe at the post-treatment 90Y-PET/CT, corresponding to a radiation-induced inflammatory lung lesion at the 3-months 18F-FDG PET/CT follow-up. 90Y-PET/CT data were used as input for Monte-Carlo based absorbed dose estdamage occurred at significantly higher absorbed doses than those considered for single administration or cumulative lung dose delivered during TARE.Patient-specific quality assurance (PSQA) of volumetric modulated arc therapy (VMAT) to assure accurate treatment delivery is resource-intensive and time-consuming. Recently, machine learning has been increasingly investigated in PSQA results prediction. However, the classification performance of models at different criteria needs further improvement and clinical validation (CV), especially for predicting plans with low gamma passing rates (GPRs). In this study, we developed and validated a novel multi-task model called autoencoder based classification-regression (ACLR) for VMAT PSQA. The classification and regression were integrated into one model, both parts were trained alternatively while minimizing a defined loss function. The classification was used as an intermediate result to improve the regression accuracy. Different tasks of GPRs prediction and classification based on different criteria were trained simultaneously. Balanced sampling techniques were used to improve the prediction accuracy and classif virtual VMAT QA.Current guidelines for administered activity (AA) in pediatric nuclear medicine imaging studies are based on a 2016 harmonization of the 2010 North American Consensus guidelines and the 2007 European Association of Nuclear Medicine pediatric dosage card. These guidelines assign AA scaled to patient body mass, with further constraints on maximum and minimum values of radiopharmaceutical activity. These guidelines, however, are not formulated based upon a rigor-ous evaluation of diagnostic image quality. In a recent study of the renal cortex imaging agent 99mTc-DMSA (Li Y et al 2019), body mass-based dosing guidelines were shown to not give the same level of image quality for patients of differing body mass. Their data suggest that patient girth at the level of the kidneys may be a better morphometric parameter to consider when selecting AA for renal nuclear medicine imaging. The objective of the present work was thus to develop a dedicated series of computational phantoms to support image quality and organ dos-olds) for 99mTc-MAG3. Using tallies of photon exit fluence as a rough surrogate for uniform image quality, our study demonstrated that through body region-of-interest optimization of AA, there is the potential for further dose and risk reductions of between factors of 1.5 to 3.0 beyond simple weight-based dosing guidance.Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. check details Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process.