Laparoscopic management of mesenteric cyst: a case report

Mesenteric cysts are rare intra-abdominal lesions with variable clinical symptoms and signs that make pre-operative diagnosis difficult. Optimal treatment is surgical excision of the cyst with laparotomy or laparoscopy. We present a case of mesenteric cyst that was misdiagnosed as para-ovarian cyst and managed laparoscopically by gynaecologists

This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation

Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the context of image-based Clinical Decision Support (CDS) systems. To this end, Generative Adversarial Networks (GANs) have been mainly applied to support the algorithm training process by generating synthetic images for data augmentation. However, in the field of Wireless Capsule Endoscopy (WCE), the limited content diversity and size of existing publicly available annotated datasets adversely affect both the training stability and synthesis performance of GANs. In this paper a novel Variational Autoencoder (VAE) architecture is proposed for WCE image synthesis, namely ‘This Intestine Does not Exist’ (TIDE). This is the first VAE architecture comprising multiscale feature extraction convolutional blocks and residual connections. Its advantage is that it enables the generation of high-quality and diverse datasets even with a limited number of training images. Contrary to the current approaches, which are oriented towards the augmentation of the available datasets, this study demonstrates that using TIDE, real WCE datasets can be fully substituted by artificially generated ones, without compromising classification performance of CDS. It performs a spherical experimental evaluation study that covers both quantitative and qualitative aspects, including a user evaluation study performed by WCE specialists, which validate from a medical viewpoint that both the normal and abnormal WCE images synthesized by TIDE are sufficiently realistic. The quantitative results obtained by comparative experiments validate that the proposed architecture outperforms the state-of-the-art

A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment

Perioperative radiotherapy versus surgery alone for retroperitoneal sarcomas: a systematic review and meta-analysis

There is no clear evidence on whether radiotherapy (RT) improves treatment result in patients with retroperitoneal sarcomas (RPS)

The Role of SNHG15 in the Pathogenesis of Hepatocellular Carcinoma

Long non-coding RNAs (lncRNAs) are transcripts of more than 200 nucleotides which cannot be translated into proteins. Small nucleolar RNA host gene 15 (SNHG15) is a lncRNA whose dysregulation has been found to have an important impact on carcinogenesis and affect the prognosis of cancer patients in various cancer types. Hepatocellular carcinoma (HCC) is one of the most common cancers with a poor long-term prognosis, while the best prognostic factor of the disease is its early diagnosis and surgery. Consequently, the investigation of the mechanisms of hepatocarcinogenesis, as well as the discovery of efficient molecular markers and therapeutic targets are of great significance. An extensive literature search was performed in MEDLINE in order to identify clinical studies that tried to reveal the role of SNHG15 in HCC. We used keywords such as ‘HCC’, ‘hepatocellular carcinoma’, ‘SNHG15’ and ‘clinical study’. Finally, we included four studies written in English, published during the period 2016–2021. It was revealed that SNHG15 is related to the appearance of HCC via different routes and its over-expression affects the overall survival of the patients. More assays are required in order to clarify the potential role of SNHG15 as a prognostic tool and therapeutic target in HCC