Using apparent diffusion coefficient maps and radiomics to predict pathological grade in upper urinary tract urothelial carcinoma.

Background: The apparent diffusion coefficient (ADC) has been reported as a quantitative biomarker for assessing the aggressiveness of upper urinary tract urothelial carcinoma (UTUC), but it has typically been used only with mean ADC values. This study aims to develop a radiomics model using ADC maps to differentiate UTUC grades by incorporating texture features and to compare its performance with that of mean ADC values.

Methods: A total of 215 patients with histopathologically confirmed UTUC were enrolled retrospectively and divided into training and test sets.

Prognostic Value of Left Ventricular Myocardial Strain Parameters Derived from Cardiac Magnetic Resonance Feature Tracking Technique in Light-Chain Cardiac Amyloidosis Patients: A Pilot Study.

Background: Previous research on the prognostic implications of left ventricular myocardial strain using cardiac magnetic resonance feature tracking (CMR-FT) in light-chain cardiac amyloidosis (AL-CA) has shown promising potential. This study aimed to evaluate the prognostic significance of global and segmental left ventricular myocardial strain in AL-CA patients, specifically analyzing the American Heart Association's 16 segments.

Methods: A total of 75 consecutive patients (50 men, mean age: 55.

Measurement of ureteral length: Comparison of deep learning-based method and other estimation methods on CT and KUB.

Background: Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting.

Purpose: Utilize a deep learning approach to measure ureter length on CT urography (CTU) images and compare the obtained results with those derived from other estimation methods.

Methods: In a retrospective cohort (cohort A, n = 411), CTU images were collected and used to develop a 3D deep learning model for the segmentation of bilateral ureters.

A prediction model to differentiate transient ischemia from irreversible transmural necrosis in closed-loop small bowel obstruction.

Introduction: The study aims to develop a prediction model to differentiate transient ischemia from irreversible transmural necrosis in closed-loop small bowel obstruction (CL-SBO).

Methods: A total of 180 participants with CL-SBO between January 2010 and December 2019, of which 122 had complete radiologic data, were included to investigate the significant clinical and imaging characteristics for differentiating patients with necrosis from transient ischemia. A logistic regression model was developed and validated.

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

Background: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge.

Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

Objectives: To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view.

Methods: A total of 1431 consecutive Laurin views of the PFJ were retrospectively collected and divided into two parts: (1) the model development dataset (dataset 1, n = 1230) and (2) the hold-out test set (dataset 2, n = 201). Dataset 1 was used to develop the U-shaped fully convolutional network (U-Net) model to segment the landmarks of the PFJ.

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

Background: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs.

Purpose: To develop and assess the performance of a DL-based approach for diagnosing and grading PFOA on axial radiographs.

The intragenic mRNA-microRNA regulatory network during telogen-anagen hair follicle transition in the cashmere goat.

It is widely accepted that the periodic cycle of hair follicles is controlled by the biological clock, but the molecular regulatory mechanisms of the hair follicle cycle have not been thoroughly studied. The secondary hair follicle of the cashmere goat is characterized by seasonal periodic changes throughout life. In the hair follicle cycle, the initiation of hair follicles is of great significance for hair follicle regeneration.

Expression of selenium-independent glutathione peroxidase 5 (GPx5) in the epididymis of Small Tail Han sheep.

Objective: Selenium-independent glutathione peroxidase (GPx5) is specifically expressed in the mammalian epididymis and plays an important role in protecting sperm from reactive oxygen species and lipid peroxidation damage. This study investigates GPx5 expression in the epididymis of Small Tail Han sheep.

Methods: GPx5 expression was studied in three age groups: lamb (2 to 3 months), young (8 to 10 months), and adult (18 to 24 months).

Expression of Vimentin in hair follicle growth cycle of inner Mongolian Cashmere goats.

Background: The growth of Inner Mongolian Cashmere goat skin hair follicle exhibits a periodic growth pattern. The hair growth cycle is distinguished as telogen, anagen, and catagen stages. The role of vimentin in the growth process of hair follicles is evident.