Numerical Study of Photoacoustic Pressure for Cancer Therapy

Gold Nanoparticles as Photothermal Agent in Cancer Therapy: Theoretical Study of Concentration and Agglomeration Effects on Temperature

One promising cancer therapy is related to the treatment of diseased cells through thermal ablation by an individual or an agglomeration of nanoparticles acting as photothermal agents. The main principle of such a therapy consists in the photo-energy absorption by the nanoparticles and its conversion into heat in order to kill the biological media/cells in the neighboring regions of such a photothermal agent. Nevertheless, such a therapy must preserve the surrounding healthy cells (or biological media). In case of agglomerates of nanoparticles, the local concentrations of nanoparticles may increase the temperature locally. In this paper, we use the finite element method to calculate the temperature elevation for agglomerations of nanoparticles in a biological medium/cell. The positions of nanoparticles, forming the agglomerates, are randomly generated. The temperature elevation for such agglomerations of nanoparticles is then analyzed. We show that the control of the concentration of nanoparticles can preserve the efficiency of the thermal agent, but with limited risk of damage to the surrounding biological media/cells.

Gold Nanoparticles as a Photothermal Agent in Cancer Therapy: The Thermal Ablation Characteristic Length

In cancer therapy, the thermal ablation of diseased cells by embedded nanoparticles is one of the known therapies. It is based on the absorption of the energy of the illuminating laser by nanoparticles. The resulting heating of nanoparticles kills the cell where these photothermal agents are embedded. One of the main constraints of this therapy is preserving the surrounding healthy cells. Therefore, two parameters are of interest. The first one is the thermal ablation characteristic length, which corresponds to an action distance around the nanoparticles for which the temperature exceeds the ablation threshold. This critical geometric parameter is related to the expected conservation of the body temperature in the surroundings of the diseased cell. The second parameter is the temperature that should be reached to achieve active thermal agents. The temperature depends on the power of the illuminating laser, on the size of nanoparticles and on their physical properties. The purpose of this paper is to propose behavior laws under the constraints of both the body temperature at the boundary of the cell to preserve surrounding cells and an acceptable range of temperature in the target cell. The behavior laws are deduced from the finite element method, which is able to model aggregates of nanoparticles. We deduce sensitivities to the laser power and to the particle size. We show that the tuning of the temperature elevation and of the distance of action of a single nanoparticle is not significantly affected by variations of the particle size and of the laser power. Aggregates of nanoparticles are much more efficient, but represent a potential risk to the surrounding cells. Fortunately, by tuning the laser power, the thermal ablation characteristic length can be controlled.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.