A Case of a Giant Siliconoma Mimicking Localized Breast Cancer.

Silicone gel-filled breast implants are widely used for breast augmentation and reconstruction after mastectomy. However, there are some known complications associated with silicone implants: Leakage and migration of silicone particles from the implant cause a granulomatous reaction. Granulomas may present as masses with features of malignancy on breast MRI.

[Dysmenorrhea and pelvic pain secondary to an accessory and cavitated uterine mass].

An accessory and cavitated uterine mass (ACUM) is a rare anomaly with an embryological origin of dysfunctionning female gubernaculum. It is an accessory mass internally lined with normal endometrium, separated from the uterine cavity and located near the insertion of the round ligament. ACUM's clinical manifestations are severe dysmenorrhea and/or chronic pelvic pain.

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

Purpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.

Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).

A review in radiomics: Making personalized medicine a reality via routine imaging.

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine.

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams.