Emerging Therapeutic Strategies in Breast Cancer.

The field of medical oncology is experiencing a period of rapid evolution owing to advances in the fields of genomics, tumor biology, and immunology. These disciplines have provided valuable insights into the heterogeneity between breast tumors, key oncogenic drivers, and the role of the immune system in the natural history of breast cancer. This knowledge is translating into many novel therapeutic strategies using personalized medicines, targeted drug delivery systems, and immunomodulatory agents in the treatment of both the early and metastatic stages of the disease.

Immunomodulatory drugs in the treatment of myelodysplastic syndromes.

Purpose Of Review: The aim of this article is to discuss the proposed mechanisms of action, clinical trial data, and clinical implications for use of the immunomodulatory drugs in the treatment of myelodysplastic syndrome.

Recent Findings: The immunomodulatory drugs are a novel, nonteratogenic class of thalidomide analogues that are more potent and less toxic than the parent compound. Lenalidomide, a second generation immunomodulatory drug, has shown substantial remitting activity in myelodysplastic syndrome that is karyotype-dependent.

The role of lenalidomide in the treatment of patients with chromosome 5q deletion and other myelodysplastic syndromes.

Purpose Of Review: The aim of this article is to discuss the relevant pathobiologic effects of lenalidomide and the most recent clinical evidence to support its use in patients with myelodysplastic syndrome.

Recent Findings: Lenalidomide is an immunomodulatory agent with biological activity in several hematologic malignancies, including myelodysplastic syndrome. The precise mechanism yielding benefit in patients with myelodysplastic syndrome and 5q- syndrome is not clear, but various molecular and pathogenic targets have been identified.

Mutagenesis of Glycine 179 modulates both catalytic efficiency and reduced pyridine nucleotide specificity in cytochrome b5 reductase.

Cytochrome b5 reductase (cb5r), a member of the ferredoxin:NADP+ reductase family of flavoprotein transhydrogenases, catalyzes the NADH-dependent reduction of cytochrome b5. Within this family, a conserved "GxGxxP" sequence motif has been implicated in binding reduced pyridine nucleotides. However, Glycine 179, a conserved residue in cb5r primary structures, precedes this six-residue "180GxGxxP185" motif that has been identified as binding the adenosine moiety of NADH.

False-positive reduction in CAD mass detection using a competitive classification strategy.

High false-positive (FP) rate remains to be one of the major problems to be solved in CAD study because too many false-positively cued signals will potentially degrade the performance of detecting true-positive regions and increase the call-back rate in CAD environment. In this paper, we proposed a novel classification method for FP reduction, where the conventional "hard" decision classifier is cascaded with a "soft" decision classification with the objective to reduce false-positives in the cases with multiple FPs retained after the "hard" decision classification. The "soft" classification takes a competitive classification strategy in which only the "best" ones are selected from the pre-classified suspicious regions as the true mass in each case.

A statistical methodology for mammographic density detection.

A statistical methodology is presented based on a chi-square probability analysis that allows the automated discrimination of radiolucent tissue (fat) from radiographic densities (fibroglandular tissue) in digitized mammograms. The method is based on earlier work developed at this facility that shows mammograms may be considered as evolving from a linear filtering operation where a random input field is passed through a 1/f filtering process. The filtering process is reversible which allows the solution of the input field with knowledge obtained from the raw image (the output).

On the statistical nature of mammograms.

We show that digitized mammograms can be considered as evolving from a simple process. A given image results from passing a random input field through a linear filtering operation, where the filter transfer function has a self-similar characteristic. By estimating the functional form of the filter and solving the corresponding filtering equation, the analysis shows that the input field gray value distribution and spectral content can be approximated with parametric methods.