Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.
In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80K×70K pixels -far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: 1) detecting cancerous regions and 2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2×1.75 cm 2 ) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8 µm per pixel). This motivates the following algorithm:Step 1) glands are segmented, Step 2) the segmented glands are classified as malignant or benign, and Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate both priors into our algorithm and rigorously evaluate system performance, extracting statistics from over 6000 simulations run across 40 RP specimens. Perhaps the most indicative result is as © 2010 Elsevier B.V. All rights reserved. * Corresponding authors jpmonaco@rci.rutgers.edu (James P. Monaco), anantm@rci.rutgers.edu (Anant Madabhushi).Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptMed Image Anal. Author manuscript; available in PMC 2011 August 1.
A new flow reactor has been developed that allows the study of heterogeneous kinetics on an aqueous surface coated by an organic monolayer. Computational fluid dynamics simulations have been used to determine the flow characteristics for various experimental conditions. In addition a mathematical framework has been developed to derive the true first-order wall loss rate coefficient, k(1st)(w), from the experimentally observed wall loss rate, k(obs). Validation of the new flow reactor is performed by measuring the uptake of O(3) by canola oil as a function of pressure and flow velocity and the reactive uptake coefficients of N(2)O(5) by aqueous 60 wt % and 80 wt % H(2)SO(4). Using this new flow reactor, we also determined the reactive uptake coefficient of N(2)O(5) on aqueous 80 wt % H(2)SO(4) solution coated with an 1-octadecanol (C(18)H(37)OH) monolayer. The uptake coefficient was determined as (8.1 +/- 3.2) x 10-4, which is about 2 orders of magnitude lower compared to the reactive uptake coefficient on a pure aqueous 80 wt % H(2)SO(4) solution. Our measured reactive uptake coefficient can be considered as a lower limit for the reactive uptake coefficient of aqueous aerosols coated with organic monolayers in the atmosphere, because in the atmosphere organic monolayers will likely also consist of surfactants with shorter lengths and branched structures which will have a smaller overall effect.
We propose a novel and accurate method based on ultrasound RF time series analysis and an extended version of support vector machine classification for generating probabilistic cancer maps that can augment ultrasound images of prostate and enhance the biopsy process. To form the RF time series, we record sequential ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. We show that RF time series acquired from agar-gelatin-based tissue mimicking phantoms, with difference only in the size of cell-mimicking microscopic glass beads, are distinguishable with statistically reliable accuracies up to 80.5%. This fact indicates that the differences in tissue microstructures affect the ultrasound RF time series features. Based on this phenomenon, in an ex vivo study involving 35 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. We report an area under receiver operating characteristic curve of 0.95 in tenfold cross validation and 0.82 in leave-one-patient-out cross validation for detection of prostate cancer.
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.
This paper presents the prototype for an augmented reality haptic simulation system with potential for spinal needle insertion training. The proposed system is composed of a torso mannequin, a MicronTracker2 optical tracking system, a PHANToM haptic device, and a graphical user interface to provide visual feedback. The system allows users to perform simulated needle insertions on a physical mannequin overlaid with an augmented reality cutaway of patient anatomy. A tissue model based on a finite-element model provides force during the insertion. The system allows for training without the need for the presence of a trained clinician or access to live patients or cadavers. A pilot user study demonstrates the potential and functionality of the system.
Purpose A randomized controlled trial was carried out to determine whether Perk Tutor, a computerized training platform that displays an ultrasound image and real-time needle position in a three-dimensional (3D) anatomical model, would benefit residents learning ultrasound-guided lumbar puncture (LP) in simulation phantoms with abnormal spinal anatomy. Methods Twenty-four residents were randomly assigned to either the Perk Tutor (P) or the Control (C) group and asked to perform an LP with ultrasound guidance on parttask trainers with spinal pathology. Group P was trained with the 3D display along with conventional ultrasound imaging, while Group C used conventional ultrasound only. Both groups were then tested solely with conventional ultrasound guidance on an abnormal spinal model not previously seen. We measured potential tissue damage, needle path in tissue, total procedure time, and needle insertion time. Procedural success rate was a secondary outcome.
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