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.

The anatomical basis for surgical intervention in myositis ossificans-A systematic review.

Myositis ossificans (MO) is a benign condition characterized by heterotrophic bone formation, most commonly within muscle tissue. Multiple types have been described, the most predominant being myositis ossificans circumscripta, which occurs in response to trauma. Myositis ossificans cases reported in the literature were reviewed systematically.

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.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Background: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.