The objective of this overview was to critically evaluate the effect of polyethylene glycol (PEG)-based hydrogel spacers during prostate brachytherapy with regard to dosimetric and clinical benefits, as well as procedure-related toxicity. METHODS AND MATERIALS: A systematic search in the PubMed database was performed. RESULTS: A total of 12 studies, involving 615 patients with PEG hydrogel injection, were included. Overall, patients well tolerated the implantation of PEG hydrogel spacers with an excellent safety profile. However, although there were some procedure-related complications, rates of these complications were very rare. Toxicities related to the spacer were limited to Grade 1 rectal discomfort and pain (9/615 patients), Grade 2 rectal ulceration (1 in 615 patients), perineal abscess (1 in 615 patients), and bacterial prostatitis (2/615 patients) according to Common Terminology Criteria for Adverse Events v4.0 grading scheme. The application of PEG hydrogel spacers significantly reduced radiation doses to the rectum during prostate brachytherapy in the different setting. Although there was no prospective randomized clinical trial, retrospective studies showed that reducing rectal doses by the implantation of PEG hydrogel may result in an improvement in rectal toxicity. CONCLUSIONS: The insertion of hydrogel spacers is safe, resulting in a significant decrease in rectal doses. This may lead to a reduction in rectal or gastrointestinal toxicity. Prospective randomized clinical trials are warranted to confirm the clinical impact of rectal dosimetric improvements.
Cancer stem cells (CSCs) have been identified as the main center of tumor therapeutic resistance. They are highly resistant against current cancer therapy approaches particularly radiation therapy (RT). Recently, a wide spectrum of physical methods has been proposed to treat CSCs, including high energetic particles, hyperthermia (HT), nanoparticles (NPs) and combination of these approaches. In this review article, the importance and benefits of the physical CSCs therapy methods such as nanomaterial-based heat treatments and particle therapy will be highlighted.
Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.
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