Publications by authors named "Y Kurata"
Background: Recent studies have focused on evaluating the biomarker value of textural features in radiological images. Our study investigated whether or not a texture analysis of computed tomographic colonography (CTC) images could be a novel biomarker for colorectal cancer (CRC).
Methods: This retrospective study investigated 263 patients with CRC who underwent contrast-enhanced CTC (CE-CTC) before curative surgery between January 2014 and December 2017.
Background: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used in gynecologic MRI. However, their imaging qualities are limited compared to Turbo Spin Echo (TSE) with antispasmodics. Even with antispasmodics, TSE can be artifact-affected, necessitating a rapid backup sequence.
View Article and Find Full Text PDFPurpose: The purposes of the study are to assess the diagnostic performance of preoperative imaging for staging factors in gastric-type endocervical adenocarcinoma (GEA) and to compare the performance for GEA with that of usual-type endocervical adenocarcinoma (UEA) among patients preoperatively deemed locally early stage (DLES) (< T2b without distant metastasis).
Materials And Methods: For this multi-center retrospective study, 58 patients were enrolled. All had undergone MRI with or without CT and FDG PET-CT preoperatively and had been pathologically diagnosed with GEA at five institutions.
Article Synopsis
- * The study investigates what influences compliance with these laws using various factors like personal benefits, economic incentives, and government effectiveness, gathering data from 251 participants through surveys.
- * Findings indicate that personal benefits and government systems are crucial in shaping perceptions of environmental policies and awareness, highlighting the need for improved education and enforcement to enhance air quality and align with sustainability goals in Lahore.
Article Synopsis
- A deep learning (DL) model using Vision Transformer (ViT) was created to automatically diagnose muscle-invasive bladder cancer (MIBC) from MRI scans.
- The study utilized data from multiple institutions, with a training dataset of 170 patients and a test dataset of 53 patients, comparing the ViT model's performance to conventional convolutional neural networks (CNNs) and radiologists' assessments.
- The results showed that the ViT model significantly outperformed CNNs in diagnostic accuracy, achieving an area under the curve (AUC) of 0.872, which is comparable to that of junior radiologists.