Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271004 | PMC |
| http://dx.doi.org/10.1016/j.ekir.2020.03.026 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The efficacy of novel biomarkers for the early detection and management of acute kidney injury: A systematic review.
PLoS One
January 2025
College of Medicine, King Faisal University, Alahsa, Saudi Arabia.
Acute kidney injury (AKI) is a frequent clinical complication lacking early diagnostic tests and effective treatments. Novel biomarkers have shown promise for enabling earlier detection, risk stratification, and guiding management of AKI. We conducted a systematic review to synthesize evidence on the efficacy of novel biomarkers for AKI detection and management.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Developing a novel model for predicting overall survival in late-onset colon adenocarcinoma patients based on LODDS: a study based on the SEER database and external validation.
Discov Oncol
January 2025
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China.
Aim: To construct a predictive model based on the LODDS stage established for patients with late-onset colon adenocarcinoma to enhance survival stratification.
Methods: Late-onset colon adenocarcinoma data were obtained from the public database. After determining the optimal LODDS truncation value for the training set via X-tile software, we created a new staging system by integrating the T stage and M stage.
Comprehensive evaluation of genomic and functional assays for homologous recombination deficiency with high-grade epithelial ovarian cancer: Platinum sensitivity and prognosis.
Int J Gynecol Cancer
January 2025
Fudan University Shanghai Cancer Center, Department of Gynecologic Oncology, Shanghai, China; Fudan University, Shanghai Medical College, Department of Oncology, Shanghai, China. Electronic address:
Objective: Homologous recombination deficiency assays, guiding treatment of poly (adenosine diphosphate ribose) polymerase inhibitors, are increasingly applied in clinics. This study aimed to evaluate the predictive performance of homologous recombination deficiency status at genomic and functional perspective on the efficacy of platinum-based chemotherapy in ovarian cancer.
Methods: Between 2016 and 2019, 134 patients with high-grade ovarian cancer were retrospectively analyzed.
Background: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!