Adenocarcinoma of the lungAdenocarcinoma of the lung is the most common type of lung cancer, and like other forms of lung cancer, it is characterized by distinct cellular and molecular features. It is classified as one of several non-small cell lung cancers (NSCLC), to distinguish it from small cell lung cancer which has a different behavior and prognosis. Lung adenocarcinoma is further classified into several subtypes and variants. The signs and symptoms of this specific type of lung cancer are similar to other forms of lung cancer, and patients most commonly complain of persistent cough and shortness of breath.
BiomarkerIn biomedical contexts, a biomarker, or biological marker, is a measurable indicator of some biological state or condition. Biomarkers are often measured and evaluated using blood, urine, or soft tissues to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers are used in many scientific fields. Biomarker (medicine)Biomarkers used in the medical field, are a part of a relatively new clinical toolset categorized by their clinical applications.
Support vector machineIn machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
Hyperbolic geometryIn mathematics, hyperbolic geometry (also called Lobachevskian geometry or Bolyai–Lobachevskian geometry) is a non-Euclidean geometry. The parallel postulate of Euclidean geometry is replaced with: For any given line R and point P not on R, in the plane containing both line R and point P there are at least two distinct lines through P that do not intersect R. (Compare the above with Playfair's axiom, the modern version of Euclid's parallel postulate.) The hyperbolic plane is a plane where every point is a saddle point.
Standard anatomical positionThe standard anatomical position, or standard anatomical model, is the scientifically agreed upon reference position for anatomical location terms. Standard anatomical positions are used to standardise the position of appendages of animals with respect to the main body of the organism. In medical disciplines, all references to a location on or in the body are made based upon the standard anatomical position. A straight position is assumed when describing a proximo-distal axis (towards or away from a point of attachment).
Molecular imagingMolecular imaging is a field of medical imaging that focuses on imaging molecules of medical interest within living patients. This is in contrast to conventional methods for obtaining molecular information from preserved tissue samples, such as histology. Molecules of interest may be either ones produced naturally by the body, or synthetic molecules produced in a laboratory and injected into a patient by a doctor. The most common example of molecular imaging used clinically today is to inject a contrast agent (e.
Coefficient of variationIn probability theory and statistics, the coefficient of variation (COV), also known as Normalized Root-Mean-Square Deviation (NRMSD), Percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is defined as the ratio of the standard deviation to the mean (or its absolute value, , and often expressed as a percentage ("%RSD"). The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay.
Image analysisImage analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.
CovarianceIn probability theory and statistics, covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the lesser values (that is, the variables tend to show similar behavior), the covariance is positive. In the opposite case, when the greater values of one variable mainly correspond to the lesser values of the other, (that is, the variables tend to show opposite behavior), the covariance is negative.
Principal component analysisPrincipal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.
