The growing amount of electronic data collected from patient care and clinical trials is motivating the creation of national repositories where multiple institutions share data about their patient cohorts. Such efforts aim to provide sufficient sample sizes for data mining and predictive modeling, ultimately improving treatment recommendations and patient outcome prediction. While these repositories offer the potential to improve our understanding of a disease, potential issues need to be addressed to ensure that multi-site data and resultant predictive models are useful to non-contributing institutions. In this paper we examine the challenges of utilizing National Cancer Institute datasets for modeling glioblastoma multiforme. We created several types of prognostic models and compared their results against models generated using data solely from our institution. While overall model performance between the data sources was similar, different variables were selected during model generation, suggesting that mapping data resources between models is not a straightforward issue.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540487 | PMC |
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Anim Cogn
January 2025
Neuroscience Department, Oberlin College, 173 Lorain St, Oberlin, OH, USA.
Keeping track of time intervals is a crucial aspect of behavior and cognition. Many theoretical models of how the brain times behavior make predictions for steady-state performance of well-learned intervals, but the rate of learning intervals in these models varies greatly, ranging from one-shot learning to learning over thousands of trials. Here, we explored how quickly rats and mice adapt to changes in interval durations using a serial fixed-interval task.
View Article and Find Full Text PDFClin Exp Med
January 2025
Department of Clinical Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland.
Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers. This multicenter study involved 116 advanced RCC patients treated with NIVO + IPI across nine oncology centers in Poland.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFFunct Integr Genomics
January 2025
Department of Oncology, the First People's Hospital of Qujing City/the Qujing Affiliated Hospital of Kunming Medical University, 1 Yuanlin Road, Qujing, Yunnan, China.
Background: T cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.
Methods: We acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!