An in silico study on the detectability of field cancerization through parenchymal analysis of digital mammograms.

BACKGROUND: Parenchymal analysis has shown promising performance for the assessment of breast cancer risk through the characterization of the texture features of mammography images. However, the working principles behind this practice are yet not well understood. Field cancerization is a phenomenon associated with genetic and epigenetic alterations in large volumes of cells, putting them on a path of malignancy before the appearance of recognizable cancer signs. Evidence suggests that it can induce changes in the biochemical and optical properties of the tissue. PURPOSE: The aim of this work was to study whether the extended genetic mutations and epigenetic changes due to field cancerization, and the impact they have on the biochemistry of breast tissues are detectable in the radiological patterns of mammography images. METHODS: An in silico experiment was designed, which implied the development of a field cancerization model to modify the optical tissue properties of a cohort of 60 voxelized virtual breast phantoms. Mammography images from these phantoms were generated and compared with images obtained from their non-modified counterparts, that is, without field cancerization. We extracted 33 texture features from the breast area to quantitatively assess the impact of the field cancerization model. We analyzed the similarity and statistical equivalence of texture features with and without field cancerization using the t-test, Wilcoxon sign rank test and Kolmogorov-Smirnov test, and performed a discrimination test using multinomial logistic regression analysis with lasso regularization. RESULTS: With modifications of the optical tissue properties on 3.9% of the breast volume, some texture features started to fail to show equivalence (p < 0.05). At 7.9% volume modification, a high percent of texture features showed statistically significant differences (p < 0.05) and non-equivalence. At this level, multinomial logistic regression analysis of texture features showed a statistically significant performance in the discrimination of mammograms from breasts with and without field cancerization (AUC = 0.89, 95% CI: 0.75-1.00). CONCLUSIONS: These results support the idea that field cancerization is a feasible underlying working principle behind the distinctive performance of parenchymal analysis in breast cancer risk assessment.

Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment.

BACKGROUND AND OBJECTIVES: The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today. METHODS: We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face. RESULTS: Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered. CONCLUSIONS: Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.

Nanostructured Titanium Dioxide Surfaces for Electrochemical Biosensing.

TiO2 electrochemical biosensors represent an option for biomolecules recognition associated with diseases, food or environmental contaminants, drug interactions and related topics. The relevance of TiO2 biosensors is due to the high selectivity and sensitivity that can be achieved. The development of electrochemical biosensors based on nanostructured TiO2 surfaces requires knowing the signal extracted from them and its relationship with the properties of the transducer, such as the crystalline phase, the roughness and the morphology of the TiO2 nanostructures. Using relevant literature published in the last decade, an overview of TiO2 based biosensors is here provided. First, the principal fabrication methods of nanostructured TiO2 surfaces are presented and their properties are briefly described. Secondly, the different detection techniques and representative examples of their applications are provided. Finally, the functionalization strategies with biomolecules are discussed. This work could contribute as a reference for the design of electrochemical biosensors based on nanostructured TiO2 surfaces, considering the detection technique and the experimental electrochemical conditions needed for a specific analyte.

Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment.

In recent years, mammographic image analysis has shown great potential for breast cancer risk assessment. The aim of risk assessment is to predict how likely a woman is to develop breast cancer in the future. Several studies suggest that computerized parenchymal analysis of mammograms can be utilized as an independent imaging biomarker of breast cancer. Parenchymal analysis consists of the quantitative assessment of visual texture patterns in mammograms to infer the level of risk. In spite of substantial evidence of the association between parenchymal patterns and breast cancer risk, its biological foundations remain poorly understood. In this work, we draw a hypothesis that links the field cancerization (FC) with breast cancer risk assessment based on the parenchymal analysis. In the literature, the FC is interpreted as a biochemical anomaly amplification in otherwise healthy cells due to the effect of pre-cancerous transformed cells in surrounding regions. Our hypothesis is that these biochemical anomaly amplifications change the cellular micro-environment which, in turn, alter tissue responses to X-ray radiation. As a result, it is reasonable to think that these changes influence the interaction of X-rays with parenchymal - the functional - breast tissue thus enabling cancer prediction by analyzing X-ray images of the breast. We believe that our hypothesis provides an actionable explanation as to how computerized parenchymal analysis of apparently normal mammograms can be successfully utilized for the stratification of breast cancer risk.

Conceptual density functional theory for electron transfer and transport in mesoscopic systems.

Molecular and supramolecular systems are essentially mesoscopic in character. The electron self-exchange, in the case of energy fluctuations, or electron transfer/transport, in the case of the presence of an externally driven electrochemical potential, between mesoscopic sites is energetically driven in such a manner where the electrochemical capacitance (C[small mu, Greek, macron]) is fundamental. Thus, the electron transfer/transport through channels connecting two distinct energetic (ΔE[small mu, Greek, macron]) and spatially separated mesoscopic sites is capacitively modulated. Remarkably, the relationship between the quantum conductance (G) and the standard electrochemical rate constant (kr), which is indispensable to understanding the physical and chemical characteristics governing electron exchange in molecular scale systems, was revealed to be related to C[small mu, Greek, macron], that is, C[small mu, Greek, macron] = G/kr. Accordingly, C[small mu, Greek, macron] is the proportional missing term that controls the electron transfer/transport in mesoscopic systems in a wide-range, and equally it can be understood from first principles density functional quantum mechanical approaches. Indeed the differences in energy between states is calculated (or experimentally accessed) throughout the electrochemical capacitance as ΔE[small mu, Greek, macron] = ß/C[small mu, Greek, macron], and thus constitutes the driving force for G and/or kr, where ß is only a proportional constant that includes the square of the unit electron charge times the square of the number of electron particles interchanged.

Could field cancerization be interpreted as a biochemical anomaly amplification due to transformed cells?

Field cancerization is a concept used to explain cellular and molecular alterations in tissue associated to neoplasia and cancer. This effect was proposed by Slaughter in order to explain the development of multiple primary tumors and locally recurrent cancer. The particular changes associated with this effect, in each type of cancer, have been detected even at distances greater than 10cm off the tumor, in areas classified as normal by histopathological studies. Early detection of lung, colon, and ovary cancer has been reported by the use of Partial Wave Microscopy Spectroscopy (PWS) and has been explained in terms of the field cancerization effect. Until now, field cancerization has been studied as a field effect and we hypothesize that it can be understood as an amplifying effect of biochemical abnormalities in cells, which leads us to ask the question: Could field cancerization be interpreted as a biochemical anomaly amplification due to transformed cells? We propose this question because the biochemical changes due to field cancerization alter the dynamics of molecules and cells in abnormal tissues in comparison to normal ones, these alterations modify the interaction of intracellular and extracellular medium, as well as cellular movement. We hypothesize that field cancerization when interpreted as an amplification effect can be used for the early detection of cancer by measuring the change of cell dynamics.

Density functional theory and an experimentally-designed energy functional of electron density.

We herein demonstrate that capacitance spectroscopy (CS) experimentally allows access to the energy associated with the quantum mechanical ground state of many-electron systems. Priorly, electrochemical capacitance, C[small mu, Greek, macron][ρ], was previously understood from conceptual and computational density functional theory (DFT) calculations. Thus, we herein propose a quantum mechanical experiment-based variational method for electron charging processes based on an experimentally-designed functional of the ground state electron density. In this methodology, the electron state density, ρ, and an energy functional of the electron density, E[small mu, Greek, macron][ρ], can be obtained from CS data. CS allows the derivative of the electrochemical potential with respect to the electron density, (δ[small mu, Greek, macron][ρ]/δρ), to be obtained as a unique functional of the energetically minimised system, i.e., ß/C[small mu, Greek, macron][ρ], where ß is a constant (associated with the size of the system) and C[small mu, Greek, macron][ρ] is an experimentally observable quantity. Thus the ground state energy (at a given fixed external potential) can be obtained simply as E[small mu, Greek, macron][ρ], from the experimental measurement of C[small mu, Greek, macron][ρ]. An experimental data-set was interpreted to demonstrate the potential of this quantum mechanical experiment-based variational principle.

Citología de cuello uterino e impeditividad eléctrica en la detección temprana del cáncer cervical / Cervical Cytology and Electrical Impedivity in the Early Detection of the Cervical Cancer

Se reportan los resultados de un estudio piloto de las propiedades eléctricas del tejido epitelial de cuello uterino por medio de espectroscopia de impeditividad eléctrica, con el propósito de estudiar la detección temprana de la neoplasia intraepitelial con éste método. Para ello, se midieron 636 espectros de impeditividad eléctrica en 53 pacientes de la Liga Santandereana de Lucha Contra el Cáncer, los cuales fueron comparados con las citologías cervicales. Los datos experimentales fueron ajustados al modelo de Cole-Cole con una herramienta computacional basada en algoritmos genéticos. Los resultados del estudio realizado sugieren una sensibilidad y especificidad superiores al 70%. Salud UIS 2012; 44 (2):15-19.

Are reported the results of a study about the properties of cervical epitelial tissue using electrical impeditivity spectroscopy, with the objective of studying the early detection of intraepithelial neoplasia, with this metled. 636 impedivity spectrums from 53 patients at Liga Santandereana de Lucha contra el cancer were measured and compared with cervical cytology. Experimental data were felted to the Cole-Cole model, using a computational tool based in genetic algorithms. The results of the study suggest a sensibility and specificity above 70%. Salud UIS 2012; 44 (2):15-19.

Determination of Cole-Cole parameters using only the real part of electrical impedivity measurements.

An algorithm is presented to determine the Cole-Cole parameters of electrical impedivity using only measurements of its real part. The algorithm is based on two multi-fold direct inversion methods for the Cole-Cole and Debye equations, respectively, and a genetic algorithm for the optimization of the mean square error between experimental and calculated data. The algorithm has been developed to obtain the Cole-Cole parameters from experimental data, which were used to screen cervical intra-epithelial neoplasia. The proposed algorithm was compared with different numerical integrations of the Kramers-Kronig relation and the result shows that this algorithm is the best. A high immunity to noise was obtained.