Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian.

BACKGROUND AND OBJECTIVE: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. METHODS: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. RESULTS: A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the "energy" involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. CONCLUSIONS: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.

Development of tolerant features for characterization of masses in mammograms.

In this paper, four new features for the analysis of breast masses are presented. These features were designed to be insensitive to the exact shape of the contour of the masses, so that an approximate contour, such as the one extracted via an automated segmentation algorithm, can be employed in their computation. Two of the features, Sp(SI) and Sp(GO), measure the degree of spiculation of a mass and its likelihood of being spiculated. One of these features, Sp(GO), is a measure of the relative gradient orientation of pixels that correspond to possible spicules. The other feature, Sp(SI), is based on a comparison of mutual information measures between selected components of the mammographic images. The last two features, Fz(1) and Fz(2), measure the local fuzziness of the mass margins based on points defined automatically. The features were tested for characterization (i.e. discrimination between circumscribed and spiculated masses) and diagnosis (i.e. discrimination between benign and malignant masses) of breast masses using a set of 319 masses and three different classifiers. In the characterization experiments the features produced a result of approximately 89% correct classification. In the diagnosis experiments, the performance achieved was approximately 81% of correct classification.

Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection.

A method for automatic detection of mammographic masses is presented. As part of this method, an enhancement algorithm that improves image contrast based on local statistical measures of the mammograms is proposed. After enhancement, regions are segmented via thresholding at multiple levels, and a set of features is computed from each of the segmented regions. A region-ranking system is also presented that identifies the regions most likely to represent abnormalities based on the features computed. The method was tested on 57 mammographic images of masses from the Mini-MIAS database, and achieved a sensitivity of 80% at 2.3 false-positives per image (average of 0.32 false-positives per image).

Improved dynamic-programming-based algorithms for segmentation of masses in mammograms.

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID2PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID2PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions.