Infrastructure tools to support an effective Radiation Oncology Learning Health System

PURPOSE

Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address.

METHODS

We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics.

RESULTS

The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances.

CONCLUSIONS

The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

The effects of dietary fatty acids on the postprandial triglyceride-rich lipoprotein/apoB48 receptor axis in human monocyte/macrophage cells

Intestinally produced triglyceride-rich lipoproteins (TRL) play an important role in the progression of atherosclerosis. In this study, we investigated the relevance of monounsaturated fatty acid (MUFA) and saturated fatty acid (SFA) in postprandial TRL in affecting the transcriptional activity of the apolipoprotein-B48 receptor (ApoB48R) and its functionality in human monocyte/macrophage cells. Healthy male volunteers were administered four standardized high-fat meals containing butter, high-palmitic sunflower oil, olive oil (ROO) or a mixture of vegetable and fish oils (50 g/m(2) body surface area) to obtain a panel of postprandial TRL with gradual MUFA oleic acid-to-SFA palmitic acid ratios. The increase in this ratio was linearly associated with a decrease of ApoB48R up-regulation and lipid accumulation in THP-1 and primary monocytes. ApoB48R mRNA levels and intracellular triglycerides were also lower in the monocytes from volunteers after the ingestion of the ROO meal when compared to the ingestion of the butter meal. In THP-1 macrophages, the increase in the MUFA oleic acid-to-SFA palmitic acid ratio in the postprandial TRL was linearly correlated with an increase in ApoB48R down-regulation and a decrease in lipid accumulation. We also revealed that the nuclear receptor transcription factors PPARα, PPARβ/δ, and PPARγ and the PPAR-RXR transcriptional complex were involved in sensing the proportion of MUFA oleic acid and SFA palmitic acid, and these were also involved in adjusting the transcriptional activity of ApoB48R. The results of this study support the notion that MUFA-rich dietary fats may prevent excessive lipid accumulation in monocyte/macrophage cells by targeting the postprandial TRL/ApoB48R axis.