Urine biomarkers of tubular injury do not improve on the clinical model predicting chronic kidney disease progression.
Sign Up to like & getrecommendations! Published in 2017 at "Kidney international"
DOI: 10.1016/j.kint.2016.09.003
Abstract: Few investigations have evaluated the incremental usefulness of tubular injury biomarkers for improved prediction of chronic kidney disease (CKD) progression. As such, we measured urinary kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, N-acetyl-ß-D-glucosaminidase and liver fatty acid… read more here.
Keywords: tubular injury; clinical model; disease; progression ... See more keywords
A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk
Sign Up to like & getrecommendations! Published in 2022 at "Science Translational Medicine"
DOI: 10.1126/scitranslmed.abj9625
Abstract: A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address… read more here.
Keywords: surrogate cardiovascular; risk; cardiovascular risk; clinical model ... See more keywords
Biomarker Predictors of Adverse Acute Kidney Injury Outcomes in Critically Ill Patients: The Dublin Acute Biomarker Group Evaluation Study
Sign Up to like & getrecommendations! Published in 2019 at "American Journal of Nephrology"
DOI: 10.1159/000500231
Abstract: Background: The Dublin Acute Biomarker Group Evaluation (DAMAGE) Study is a prospective 2-center observational study investigating the utility of urinary biomarker combinations for the diagnostic and prognostic assessment of acute kidney injury (AKI) in a… read more here.
Keywords: clinical model; biomarker; study; kidney injury ... See more keywords
Acute Kidney Injury following Cardiac Surgery: A Clinical Model.
Sign Up to like & getrecommendations! Published in 2019 at "Nephron"
DOI: 10.1159/000501559
Abstract: Background Scientists use preclinical models of acute kidney injury (AKI) to decipher mechanisms and develop therapy, but translation of therapies to patients remains poor. Models that better resemble patients, including those within clinical care, should… read more here.
Keywords: kidney injury; surgery; clinical model; cardiac surgery ... See more keywords
MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma.
Sign Up to like & getrecommendations! Published in 2023 at "Acta radiologica"
DOI: 10.1177/02841851231170364
Abstract: BACKGROUND Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult. PURPOSE To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with… read more here.
Keywords: venous invasion; rectal adenocarcinoma; prediction; clinical model ... See more keywords
A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population
Sign Up to like & getrecommendations! Published in 2021 at "Cancer Cell International"
DOI: 10.1186/s12935-021-01810-5
Abstract: Background This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center… read more here.
Keywords: clinical model; pulmonary nodules; model; test ... See more keywords
Application of computed tomography-based radiomics combined with clinical factors in the diagnosis of malignant degree of lung adenocarcinoma
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Thoracic Disease"
DOI: 10.21037/jtd-22-1520
Abstract: Background As an emerging technology, radiomics is being widely used in the diagnosis of early lung cancer due to its excellent diagnostic performance. However, there is a lack of studies that apply radiomics to the… read more here.
Keywords: lung adenocarcinoma; computed tomography; lung; clinical model ... See more keywords
The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2022.875761
Abstract: Purpose Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.… read more here.
Keywords: cell; machine; machine learning; lung ... See more keywords
Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2022.887546
Abstract: Objectives To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH). Materials and Methods A total of 122 patients… read more here.
Keywords: endometrial carcinoma; multimodal mri; radiomics clinical; clinical model ... See more keywords
One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2022.990156
Abstract: Purpose We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making.… read more here.
Keywords: group; strategy; clinical model; deep learning ... See more keywords
Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Physiology"
DOI: 10.3389/fphys.2021.753282
Abstract: Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.… read more here.
Keywords: response; crt; model; model driven ... See more keywords