A Bayesian Network Approach to Lung Cancer Screening: Assessing the Impact of Data Quantity, Quality, and the Combination of Data from Danish Electronic Health Records.

: Lung cancer (LC) is the leading cause of cancer mortality, making early diagnosis essential. While LC screening trials are underway globally, optimal prediction models and inclusion criteria are still lacking. This study aimed to develop and evaluate Bayesian Network (BN) models for LC risk prediction using a decade of data from Denmark.

Bivalent boosters and risk of post-acute sequelae following vaccine-breakthrough SARS-CoV-2 Omicron infection: a cohort study.

Background: Vaccination has been shown to attenuate the risk of post-acute sequelae following SARS-CoV-2 infection. However, no prior population-based studies have evaluated if updated bivalent boosters reduce risk of post-acute sequelae following Omicron-variant infection, versus ancestral vaccines.

Methods: National databases were utilised to construct a population-based cohort of adult individuals infected during Omicron-predominant transmission.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.

Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.

Skeletal muscle is independently associated with grade 3-4 toxicity in advanced stage pancreatic ductal adenocarcinoma patients receiving chemotherapy.

Background: Patients with advanced-stage pancreatic ductal adenocarcinoma (PDAC) are regularly treated with FOLFIRINOX, a chemotherapy regimen based on 5-fluorouracil, irinotecan and oxaliplatin, which is associated with high toxicity. Dosing of FOLFIRINOX is based on body surface area, risking under- or overdosing caused by altered pharmacokinetics due to interindividual differences in body composition. This study aimed to investigate the relationship between body composition and treatment toxicity in advanced stage PDAC patients treated with FOLFIRINOX.