Computational fluid dynamics has the potential to assist cardiovascular surgeons in making more accurate decisions, allowing the prediction of post-surgical outcomes, provided that pre-surgical conditions are well established. However, the application of current techniques, which are based on volume methods, is still limited to a few specialized centers. Lack of knowledge, coupled with the need for advanced computational resources, can serve as obstacles to implementation. Objective: This study aimed to develop a replicable surgical planning procedure for a simplified and clinically feasible total cavopulmonary geometry. The finite volume method was used to simulate different configurations of cavopulmonary anastomosis under continuous and pulsatile flow and thus gain a better understanding of blood behavior, energy efficiency, and shear stress in the studied regions. Two geometries were found to be efficient in distributing blood flow in a physiological manner, with adequate shear stress and energy loss. In addition to the correct placement of the anastomosis, the results underscored the need for attention regarding potential stenoses in pulmonary arteries to obtain adequate geometries. The developed method proved to be effective for early visualization of post-surgical results, particularly in complex clinical cases. Furthermore, the method contributes to a comprehensive understanding of hemodynamics in the studied area, improving the accuracy of cardiovascular surgical planning.

Univentricular congenital heart diseases represent a significant challenge in cardiology, requiring a complex staged treatment protocol involving total cavopulmonary connection via the Fontan procedure. The application of advanced numerical methods has proved fundamental to understanding post-surgical behavior, allowing the multidisciplinary team to design more efficient and physiologically realistic anatomies. However, the definition of boundary conditions to represent fluid behavior in anatomical changes following total cavopulmonary connection is still an emerging field, frequently relying on estimates and simplifications. This study aimed to identify an effective set of variables for simplifying the analysis of hemodynamic behavior in total cavopulmonary connection and reducing the requirement for extensive computational resources. A multiparameter comparative analysis of simulations was performed by using several variables under rest conditions for specific Fontan configurations, accounting for factors such as turbulent versus laminar flow, Newtonian versus non-Newtonian fluid, and rigid versus flexible vascular walls. Two sets of variables were found to provide the best results: (i) Newtonian fluid behavior, turbulent flow, steady regime, and rigid walls were found suitable when the distribution of blood flow to the pulmonary arteries is the desired result and (ii) Newtonian fluid behavior, turbulent flow, transient regime, and rigid walls were found suitable when the distribution of blood flow to pulmonary arteries, shear stress, and energy loss are equally important. The identified sets of variables provide a solid foundation for future analyses, ensuring reliable results and an efficient use of computational resources.

Background/Objectives: Radiotherapy, radiobiology, and oncology have evolved rapidly over the past six decades. This progress has generated vast but fragmented bodies of scientific evidence. The present study aimed to systematically map and interpret their conceptual and temporal development using artificial intelligence (AI)-based methods. It highlights the integration between molecular mechanisms, clinical applications, and technological innovation within a precision radiotherapy framework. Methods: A corpus of 3343 unique articles (1964–2025) was retrieved from Scopus, PubMed, and Web of Science. Records were harmonized through deduplication, lemmatization, and metadata normalization. Topic modeling using Latent Dirichlet Allocation (LDA) and co-occurrence network analysis were applied to identify dominant research axes. Semantic and temporal analyses were conducted to reveal patterns, emerging trends, and translational connections across decades. Results: Three historical phases were identified. The first was a period of limited production (1964–1990). The second showed moderate growth (1991–2010). The third, from 2011 to 2024, represented exponential expansion, with publication peaks in 2020 and 2023. LDA revealed two principal axes. The first, a clinical–anatomical axis, focused on cancer sites, treatment modalities, and prognosis. The second, a mechanistic–molecular axis, centered on DNA repair, radiosensitivity, and biomarkers. Case synthesis from 2014–2025 defined five operational classes: DNA repair and molecular response; precision oncology and genomic modeling; individual radiosensitivity; mechanisms of radioresistance; and advanced technologies such as FLASH radiotherapy and optimized brachytherapy. Conclusions: AI-driven semantic and temporal analyses showed that radiotherapy has matured into an interconnected and interdisciplinary domain. The derived Precision Radiotherapy Implementation Plan translates molecular and computational insights into clinically actionable strategies. These approaches can enhance survival, reduce toxicity, and inform equitable health policies for advanced cancer care.