Instytut Podstawowych Problemów Techniki

Abstract:
This article presents experimental, theoretical, and numerical studies of the propagation of guided ultrasonic waves in a layered epitaxial structure of garnet compounds. A microscopic model, which yields dispersion equations based on material and geometrical properties, is developed. Acoustic microscopy experiments on a YAG:Ce crystal substrate and an epitaxial structure containing LuAG:Ce single crystalline films, grown using the liquid phase epitaxy growth method onto a YAG:Ce crystal substrate, reveal distinct phase velocity behaviors. The YAG substrate exhibits consistent velocities, minimally influenced by frequency, while the epitaxial structure shows dispersion, indicating frequency-dependent phase velocities. Experimental results are compared with numerically calculated dispersion curves, showing high agreement in the low-frequency range and minor deviations at higher frequencies. An optimization procedure is developed and applied, starting with the YAG substrate and extending to the LuAG:Ce film/YAG:Ce crystal epitaxial structure. The procedure allows for the extraction of material properties, offering valuable insights into the mechanical characteristics of the all-solid-state LuAG:Ce film/YAG:Ce crystal structure. This research represents a significant advancement in understanding ultrasonic wave dynamics in layered structures, particularly unveiling previously unexplored elastic properties of LuAG:Ce single crystalline films as a well-known scintillation material.

Abstract:
This paper presents a mathematical and numerical framework for modelling and parametrising tumour evolution dynamics to enhance computer-aided diagnosis and personalised treatment. The model comprises six differential equations describing cancer cell and blood vessel concentrations, tissue stiffness, Ki-67 marker distribution, and the apparent velocity of marker propagation. These equations are coupled through S-functions with adjustable coefficients. An inverse problem approach calibrates the model by fitting adjustable coefficients to patient-specific clinical data, thereby enabling disease progression and treatment response simulations. By integrating historical and prospective patient data supported by machine learning algorithms, this framework holds promise as a robust decision-support tool for optimising therapeutic strategies.

Keywords:
Tumour modelling, Personalised treatment, Breast cancer, Navier–stokes, Evolution simulation, Machine learning

Abstract:
This study introduces a spatially distributed diffusion model based on a Navier–Stokes formulation with a pseudo-velocity field, providing a framework for modelling cellular growth dynamics within diseased tissues. Five coupled partial differential equations describe diseased cell development within a two-dimensional spatial domain over time. A pseudo-velocity field mimics biomarker concentration increasing over time and space, influencing tumour growth dynamics. An

Keywords:
Tumour growth, Cellular growth, Cancer, Navier–stokes, Diffusion, Finite element method

Abstract:
The aim of the article is to highlight the key role of artificial intelligence in modern oncology. The search for scientific publications was carried out through the following web search engines: PubMed, PMC, Web of Science, Scopus, Embase and Ebsco. Artificial intelligence plays a special role in oncology and is considered to be the future of oncology. The largest application of artificial intelligence in oncology is in diagnostics (more than 80%),
particularly in radiology and pathology. This can help oncologists not only detect cancer at an early stage but also forecast the possible development of the disease by using predictive models. Artificial intelligence plays a special role in clinical trials. AI makes it possible to
accelerate the discovery and development of new drugs, even if not necessarily successfully. This is done by detecting new molecules. Artificial intelligence enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol. This can be done by reducing population heterogeneity, or by prognostic and predictive enrichment. The effectiveness of artificial intelligence in oncology
depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.

Keywords:
artificial intelligence, intelligent oncology, cancer prediction, cancer screening