On optimal designs for clinical trials: An updated review

Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development

Radiation Therapy Medical Physics Review – Delivery, Interactions, Safety, Feasibility, and Head to Head Comparisons of the Leading Radiation Therapy Techniques

Radiation therapy uses high energy radiation to kill cancer cells. Radiation therapy for cancer treatment can take the form of photon therapy (using x-rays and gamma rays), or charged particle therapy including proton therapy and electron therapy. Within these categories, numerous methods of delivery have been developed. For example, a certain type of radiation can be administered by a machine outside of the body, called external-beam radiation therapy, or by a “seed” placed inside of the body near cancer cells, called internal radiation therapy or brachytherapy. Approximately half of all cancer patients receive radiation therapy, and the form of radiation treatment depends on the type of tumor, location of the tumor, available resources, and characteristics of the individual receiving treatment. In the current paper, we discuss and review the various forms of radiation therapy, the physics behind these treatments, the effectiveness of each treatment type compared with the others, the latest research on radiation therapy treatment, and future research directions. We found that proton therapy is the most promising and effective form of radiation therapy, with photon methods such as intensity modulated radiation therapy, 3D-conformal radiation therapy, image guided radiation therapy, and volumetric modulated radiation therapy also showing very good comparative performance

Improving treatment of glioblastoma: new insights in targeting cancer stem cells effectively

Glioblastoma is the most common primary malignant brain tumour in the adult population. Despite multimodality treatment with surgery, radiotherapy and chemotherapy, outcomes are very poor, with less than 15% of patients alive after two years. Increasing evidence suggests that glioblastoma stem cells (GSCs) are likely to play an important role in the biology of this disease and are involved in treatment resistance and tumour recurrence following standard therapy. My thesis aims to address two main aspects of this research area: 1) optimization of methods to evaluate treatment responses of GSCs and their differentiated counterparts (non-GSCs), with a particular focus on a tissue culture model that resembles more closely the tumoral niche; 2) characterization of cell division and centrosome cycle of GSCs, investigating possible differences between these cells and non-GSCs, that would allow the identification of targets for new therapeutic strategies against glioblastomas. In the first part of my project, I optimized a clonogenic survival assay, to compare sensitivity of GSCs and non-GSCs to various treatments, and I developed the use of a 3-dimentional tissue culture system, that allows analysis of features and radiation responses of these two subpopulations in the presence of specific microenvironmental factors from the tumoral niche. In the second part, I show that GSCs display mitotic spindle abnormalities more frequently than non-GSCs and that they have distinctive features with regards to the centrosome cycle. I also demonstrate that GSCs are more sensitive than non-GSCs to subtle changes in Aurora kinase A activity, which result in a rapid increase in polyploidy and subsequently in senescence, with a consistent reduction in clonogenic survival. Based on these findings, I propose that kinases involved in the centrosome cycle need to be explored as a novel strategy to target GSCs effectively and improve outcomes of glioblastoma patients

Searching standard parameters for volumetric modulated arc therapy (VMAT) of prostate cancer

Background Since December 2009 a new VMAT planning system tool is available in Oncentra® MasterPlan v3.3 (Nucletron B.V.). The purpose of this study was to work out standard parameters for the optimization of prostate cancer. Methods For ten patients with localized prostate cancer plans for simultaneous integrated boost were optimized, varying systematically the number of arcs, collimator angle, the maximum delivery time, and the gantry spacing. Homogeneity in clinical target volume, minimum dose in planning target volume, median dose in the organs at risk, maximum dose in the posterior part of the rectum, and number of monitor units were evaluated using student’s test for statistical analysis. Measurements were performed with a 2D-array, taking the delivery time, and compared to the calculation by the gamma method. Results Plans with collimator 45° were superior to plans with collimator 0°. Single arc resulted in higher minimum dose in the planning target volume, but also higher dose values to the organs at risk, requiring less monitor units per fraction dose than dual arc. Single arc needs a higher value (per arc) for the maximum delivery time parameter than dual arc, but as only one arc is needed, the measured delivery time was shorter and stayed below 2.5 min versus 3 to 5 min. Balancing plan quality, dosimetric results and calculation time, a gantry spacing of 4° led to optimal results. Conclusion A set of parameters has been found which can be used as standard for volumetric modulated arc therapy planning of prostate cancer

GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

Online adaptive radiation therapy (ART) has great promise to significantly reduce normal tissue toxicity and/or improve tumor control through real-time treatment adaptations based on the current patient anatomy. However, the major technical obstacle for clinical realization of online ART, namely the inability to achieve real-time efficiency in treatment re-planning, has yet to be solved. To overcome this challenge, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) direct aperture optimization (DAO) algorithm on graphics processing unit (GPU) based on our previous work on CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called column generation approach to deal with its extremely large dimensionality on GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and 2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our work has therefore solved a major problem in developing ultra-fast (re-)planning technologies for online ART

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data