Journal of vascular and interventional radiology : JVIR
32376183
PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome.
The international journal of medical robotics + computer assisted surgery : MRCAS
32469471
BACKGROUND: We hypothesized that the total operative time of robot myomectomy (RM) is shorter than that of laparoscopic myomectomy (LM) in cases where numerous myomas are removed, due to the faster suturing time of the robotic system. To verify this,...
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presen...
Journal of magnetic resonance (San Diego, Calif. : 1997)
33648679
Low field Nuclear Magnetic Resonance (LF-NMR) is a rich source of information for a wide range of samples types. These can be hard or soft solids, such as plastics or elastomers; bulk liquids or liquids absorbed in porous materials, and can come from...
STUDY OBJECTIVE: To show the challenging diagnosis of, and safe robotic surgical approach to, a rare case of disseminated peritoneal leiomyomatosis (DPL).
OBJECTIVE: To develop and evaluate the performance of a radiomics and machine learning model applied to ultrasound (US) images in predicting the risk of malignancy of a uterine mesenchymal lesion.
Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can damage the surrounding tissues and cause the formation of cancerous cells. In the USA, according to the...
INTRODUCTION: Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is hindered by the complex classification of patients into risk classes. This problem could be simplifie...
OBJECTIVES: To explore the feasibility and effectiveness of machine learning (ML) based on multiparametric magnetic resonance imaging (mp-MRI) features extracted from transfer learning combined with clinical parameters to differentiate uterine sarcom...
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-b...