PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

RNA aptamers represent an emerging class of pharmaceuticals with great potential for targeted cancer diagnostics and therapy. Several RNA aptamers that bind cancer cell-surface antigens with high affinity and specificity have been described. However, their clinical potential has yet to be realized. A significant obstacle to the clinical adoption of RNA aptamers is the high cost of manufacturing long RNA sequences through chemical synthesis. Therapeutic aptamers are often truncated postselection by using a trial-and-error process, which is time consuming and inefficient. Here, we used a "rational truncation" approach guided by RNA structural prediction and protein/RNA docking algorithms that enabled us to substantially truncateA9, an RNA aptamer to prostate-specific membrane antigen (PSMA),with great potential for targeted therapeutics. This truncated PSMA aptamer (A9L; 41mer) retains binding activity, functionality, and is amenable to large-scale chemical synthesis for future clinical applications. In addition, the modeled RNA tertiary structure and protein/RNA docking predictions revealed key nucleotides within the aptamer critical for binding to PSMA and inhibiting its enzymatic activity. Finally, this work highlights the utility of existing RNA structural prediction and protein docking techniques that may be generally applicable to developing RNA aptamers optimized for therapeutic use.

For the case considered, the proposed(153)Gd-based I-RSBT system has the potential to lower the urethral dose relative to HDR-BT by 29%-44% if the clinician allows a urethral dose gradient volume of 0-5 mm around the urethra to receive a dose below the prescription. A multisource approach is necessary in order to deliver the proposed (153)Gd-based I-RSBT technique in reasonable treatment times.

Cell-targeted therapies (smart drugs), which selectively control cancer cell progression with limited toxicity to normal cells, have been developed to effectively treat some cancers. However, many cancers such as metastatic prostate cancer (PC) have yet to be treated with current smart drug technology. Here, we describe the thorough preclinical characterization of an RNA aptamer (A9g) that functions as a smart drug for PC by inhibiting the enzymatic activity of prostate-specific membrane antigen (PSMA). Treatment of PC cells with A9g results in reduced cell migration/invasion in culture and metastatic disease in vivo. Importantly, A9g is safe in vivo and is not immunogenic in human cells. Pharmacokinetic and biodistribution studies in mice confirm target specificity and absence of non-specific on/off-target effects. In conclusion, these studies provide new and important insights into the role of PSMA in driving carcinogenesis and demonstrate critical endpoints for the translation of a novel RNA smart drug for advanced stage PC.

Systematic evolution of ligands by exponential enrichment (SELEX) is a powerful in vitro selection process used for over 2 decades to identify oligonucleotide sequences (aptamers) with desired properties (usually high affinity for a protein target) from randomized nucleic acid libraries. In the case of RNA aptamers, several highly complex RNA libraries have been described with RNA sequences ranging from 71 to 81 nucleotides (nt) in length. In this study, we used high-throughput sequencing combined with bioinformatics analysis to thoroughly examine the nucleotide composition of the sequence pools derived from several selections that employed an RNA library (Sel2N20) with an abbreviated variable region. The Sel2N20 yields RNAs 51 nt in length, which unlike longer RNAs, are more amenable to large-scale chemical synthesis for therapeutic development. Our analysis revealed a consistent and early bias against inclusion of adenine, resulting in aptamers with lower predicted minimum free energies (ΔG) (higher structural stability). This bias was also observed in control, "nontargeted" selections in which the partition step (against the target) was omitted, suggesting that the bias occurred in 1 or more of the amplification and propagation steps of the SELEX process.