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Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment.
Radiol Imaging Cancer
January 2025
From the Department of Radiology (A.C., A.N.Y., R.E., C.H., G.L., M.M., E.B.J., A.L.C., B.G., G.S.K., A.O.), Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy (A.C., A.N.Y., M.M., A.L.C., B.G.), Department of Surgery, Section of Urology (G.G., L.F.R., P.K.M., S.E.), Department of Pathology (T.A.), and Department of Public Health Sciences (M.G.), University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
Purpose To evaluate the use of an automated hybrid multidimensional MRI (HM-MRI)-based tool to prospectively identify prostate cancer targets before MRI/US fusion biopsy in comparison with Prostate Imaging and Reporting Data System (PI-RADS)-based multiparametric MRI (mpMRI) evaluation by expert radiologists. Materials and Methods In this prospective clinical trial (ClinicalTrials.gov registration no.
View Article and Find Full Text PDFAutomated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFHigh-quality targeted temperature management combined with decompressive craniectomy in patients with poor-grade aneurysmal subarachnoid hemorrhage: a secondary analysis of a multicenter prospective study.
Front Neurol
January 2025
Department of Neurosurgical Intensive Care Unit, Henan Provincial People's Hospital, Zhengzhou, China.
Background: The effect of targeted temperature management (TTM) combined with decompressive craniectomy (DC) on poor-grade aneurysmal subarachnoid hemorrhage (aSAH) has not been previously addressed in the literature. This study aims to investigate the therapeutic outcomes of the combination of TTM and DC in patients with poor-grade aSAH.
Methods: This study represents a secondary analysis of the Multicenter Clinical Research on Targeted Temperature Management of Poor-grade Aneurysmal Subarachnoid Hemorrhage (High-Quality TTM for PaSAH), a multicenter prospective study conducted in China.
GLUT1 as a generic biomarker enables near-infrared fluorescence molecular imaging guided precise intraoperative tumor detection in breast cancer.
Eur J Nucl Med Mol Imaging
January 2025
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Hai Dian District, Beijing, 100190, China.
Purpose: Precise tumor excision is important but challenging in breast-conserving surgery (BCS). Tumor-specific fluorescence imaging may be used for intraoperative tumor detection and, therefore, to guide precise tumor excision. The aims of this study are to develop a glucose transporter 1 (GLUT1)-targeted near-infrared fluorescence tracer and evaluate its accuracy for breast cancer detection using fresh surgical breast specimens.
View Article and Find Full Text PDFPredicting drug and target interaction with dilated reparameterize convolution.
Sci Rep
January 2025
Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, 110016, China.
Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands and targets on a small scale. However, it is difficult for the model to obtain the potential law of combining pockets or multiple binding sites on a large scale.
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