Novel targeted agents in the treatment of lung cancer

Targeted therapies in small-cell lung cancer

Small-cell lung cancer is an aggressive form of lung cancer that, overall, remains the most common cause of cancer death in the US. Some advances have been made in the treatment of small-cell lung cancer using cytotoxic chemotherapeutic agents but no truly targeted therapies are available as of yet. At present, research is focused on finding therapies that can target the specific molecular mechanisms responsible for the survival, growth and metastasis of the tumor thereby improving responses to chemotherapy and minimizing toxicity. Several new agents, such as angiogenesis inhibitors and regulators of apoptosis, have reached clinical testing and multiple others are in preclinical trials. Some of these will be discussed in this review.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.