131I Intake and Regulatory Compliance Following Administration of Capsular 131I Sodium Iodide for Treatment of Thyroid Disorders.

ABSTRACT: Intake of 131I by nuclear medicine technologists and physician Authorized Users was evaluated using bioassay data from administration of 131I sodium iodide in capsular form during a 5-year period. Maximum estimated annual intake of 131I sodium iodide, based on bioassay measurements performed at 24 hours post administration, ranged from 10.9 to 35.6 kBq for all staff. Intake by Authorized Users was higher than that by nuclear medicine technologists due to state requirement for Authorized Users to physically administer therapeutic dosages of radiopharmaceuticals. All intake values were less than 10% of the 131I thyroid ALI of 50 microcurie3 (1,850 kBq), indicating that monitoring may be discontinued for staff participating in routine administration of 131I capsules in which volatilization is not suspected. Elimination of bioassay performance has permitted more flexibility in patient scheduling and improved workflow and efficiency.

Quantification of Cerenkov Luminescence Imaging (CLI) Comparable With 3-D PET Standard Measurements.

Cerenkov luminescence imaging (CLI) is commonly performed using two-dimensional (2-D) conventional optical imaging systems for its cost-effective solution. However, quantification of CLI comparable to conventional three-dimensional positron emission tomography (PET) is challenging using these systems due to both the high attenuation of Cerenkov radiation (CR) on mouse tissue and nonexisting depth resolution of CLI using 2-D imaging systems (2-D CLI). In this study, we developed a model that estimates effective tissue attenuation coefficient and corrects the tissue attenuation of CLI signal intensity independent of tissue depth and size. To evaluate this model, we used several thin slices of ham as a phantom and placed a radionuclide (89Zr and 64Cu) inside the phantom at different tissue depths and sizes (2, 7, and 12 mm). We performed 2-D CLI and MicroPET/CT (Combined small animal PET and Computed Tomography (CT)) imaging of the phantom and in vivo mouse model after administration of 89Zr tracer. Estimates of the effective tissue attenuation coefficient (µeff) for 89Zr and 64Cu were ∼2.4 and ∼2.6 cm-1, respectively. The computed unit conversion factor to %ID/g from 2-D CLI signal was 2.74 × 10-3 µCi/radiance estimated from phantom study. After applying tissue attenuation correction and unit conversion to the in vivo animal study, an average quantification difference of 10% for spleen and 35% for liver was obtained compared to PET measurements. The proposed model provides comparable quantification accuracy to standard PET system independent of deep tissue CLI signal attenuation.

Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions.

RATIONALE AND OBJECTIVES: This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment. MATERIALS AND METHODS: Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs. RESULTS: The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility. CONCLUSIONS: The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.

A tumor-immune mathematical model of CD4+ T helper cell dependent tumor regression by oncogene inactivation.

Understanding the complex dynamics between the tumor cells and the host immune system will be key to improved therapeutic strategies against cancer. We propose an ODE-based mathematical model of both the tumor and immune system and how they respond to inactivation of the driving oncogene. Our model supports experimental results showing that cellular senescence of tumor cells is dependent on CD4+ T helper cells, leading to relapse of tumors in immunocompromised hosts.

Impact of a multiple mice holder on quantitation of high-throughput MicroPET imaging with and without Ct attenuation correction.

PURPOSE: The aim of this study is to evaluate the impact of scanning multiple mice simultaneously on image quantitation, relative to single mouse scans on both a micro-positron emission tomography/computed tomography (microPET/CT) scanner (which utilizes CT-based attenuation correction to the PET reconstruction) and a dedicated microPET scanner using an inexpensive mouse holder "hotel." METHODS: We developed a simple mouse holder made from common laboratory items that allows scanning multiple mice simultaneously. It is also compatible with different imaging modalities to allow multiple mice and multi-modality imaging. For this study, we used a radiotracer ((64)Cu-GB170) with a relatively long half-life (12.7 h), selected to allow scanning at times after tracer uptake reaches steady state. This also reduces the effect of decay between sequential imaging studies, although the standard decay corrections were performed. The imaging was also performed using a common tracer, 2-deoxy-2-[(18) F]fluoro-D-glucose (FDG), although the faster decay and faster pharmacokinetics of FDG may introduce greater biological variations due to differences in injection-to-scan timing. We first scanned cylindrical mouse phantoms (50 ml tubes) both in a groups of four at a time (multiple mice mode) and then individually (single mouse mode), using microPET/CT and microPET scanners to validate the process. Then, we imaged a first set of four mice with subcutaneous tumors (C2C12Ras) in both single- and multiple-mice imaging modes. Later, a second set of four normal mice were injected with FDG and scanned 1 h post-injection. Immediately after completion of the scans, ex vivo biodistribution studies were performed on all animals to provide a "gold-standard" to compare quantitative values obtained from PET. A semi-automatic threshold-based region of interest tool was used to minimize operator variability during image analysis. RESULTS: Phantom studies showed less than 4.5 % relative error difference between the single- and multiple-mice imaging modes of PET imaging with CT-based attenuation correction and 18.4 % without CT-based attenuation correction. In vivo animal studies (n = 4) showed <5 % (for (64)Cu, p > 0.686) and <15 % (for FDG, p > 0.4 except for brain image data p = 0.029) relative mean difference with respect to percent injected dose per gram (%ID/gram) between the single- and multiple-mice microPET imaging mode when CT-based attenuation correction is performed. Without CT-based attenuation correction, we observed relative mean differences of about 11 % for (64)Cu and 15 % for FDG. CONCLUSION: Our results confirmed the potential use of a microPET/CT scanner for multiple mice simultaneous imaging without significant sacrifice in quantitative accuracy as well as in image quality. Thus, the use of the mouse "hotel" is an aid to increasing instrument throughput on small animal scanners with minimal loss of quantitative accuracy.

In situ study of the impact of inter- and intra-reader variability on region of interest (ROI) analysis in preclinical molecular imaging.

We estimated reader-dependent variability of region of interest (ROI) analysis and evaluated its impact on preclinical quantitative molecular imaging. To estimate reader variability, we used five independent image datasets acquired each using microPET and multispectral fluorescence imaging (MSFI). We also selected ten experienced researchers who utilize molecular imaging in the same environment that they typically perform their own studies. Nine investigators blinded to the data type completed the ROI analysis by drawing ROIs manually that delineate the tumor regions to the best of their knowledge and repeated the measurements three times, non-consecutively. Extracted mean intensities of voxels within each ROI are used to compute the coefficient of variation (CV) and characterize the inter- and intra-reader variability. The impact of variability was assessed through random samples iterated from normal distributions for control and experimental groups on hypothesis testing and computing statistical power by varying subject size, measured difference between groups and CV. The results indicate that inter-reader variability was 22.5% for microPET and 72.2% for MSFI. Additionally, mean intra-reader variability was 10.1% for microPET and 26.4% for MSFI. Repeated statistical testing showed that a total variability of CV < 50% may be needed to detect differences < 50% between experimental and control groups when six subjects (n = 6) or more are used and statistical power is adequate (80%). Surprisingly high variability has been observed mainly due to differences in the ROI placement and geometry drawn between readers, which may adversely affect statistical power and erroneously lead to negative study outcomes.

Raman Labeled Nanoparticles: Characterization of Variability and Improved Method for Unmixing.

Raman spectroscopy can differentiate the spectral fingerprints of many molecules, resulting in potentially high multiplexing capabilities of Raman-tagged nanoparticles. However, accurate quantitative unmixing of Raman spectra is challenging because of potential overlaps between Raman peaks from each molecule as well as slight variations in the location, height and width of the very narrow peaks. If not accounted for properly, even minor fluctuations in the spectra may produce significant error which will ultimately result in poor unmixing accuracy. The objective of our study was to develop and validate a mathematical model of the Raman spectra of nanoparticles to unmix the contributions from each nanoparticle allowing simultaneous quantitation of several nanoparticle concentrations during sample characterization. We developed and evaluated an algorithm for quantitative unmixing of the spectra, called Narrow Peak Spectral Algorithm (NPSA) . Using NPSA, we were able to successfully unmix Raman spectra from up to 7 Raman nanoparticles after correcting for the spectral variations of 30% in intensity and shifts in peak locations of up to 10 cm-1 which is equivalent to 50% of the full width at half maximum (FWHM). We compared the performance of NPSA to the conventional least squares analysis (LS), error in NPSA is approximately 50% lower than LS. The error in estimating the relative contributions of each nanoparticle using NPSA are in the range of 10-16% for equal ratios and 13-19% for unequal ratios for unmixing of 7 composite organic - inorganic nanoparticles (COINs) whereas the errors using the traditional least squares approach were in the range of 25-38% for equal ratios and 45-68% for unequal ratios. Here, we report for the first time, the quantitative unmixing of 7 nanoparticles with maximum RMS % error less than 20%.

Informatics and standards for nanomedicine technology.

There are several issues to be addressed concerning the management and effective use of information (or data), generated from nanotechnology studies in biomedical research and medicine. These data are large in volume, diverse in content, and are beset with gaps and ambiguities in the description and characterization of nanomaterials. In this work, we have reviewed three areas of nanomedicine informatics: information resources; taxonomies, controlled vocabularies, and ontologies; and information standards. Informatics methods and standards in each of these areas are critical for enabling collaboration; data sharing; unambiguous representation and interpretation of data; semantic (meaningful) search and integration of data; and for ensuring data quality, reliability, and reproducibility. In particular, we have considered four types of information standards in this article, which are standard characterization protocols, common terminology standards, minimum information standards, and standard data communication (exchange) formats. Currently, because of gaps and ambiguities in the data, it is also difficult to apply computational methods and machine learning techniques to analyze, interpret, and recognize patterns in data that are high dimensional in nature, and also to relate variations in nanomaterial properties to variations in their chemical composition, synthesis, characterization protocols, and so on. Progress toward resolving the issues of information management in nanomedicine using informatics methods and standards discussed in this article will be essential to the rapidly growing field of nanomedicine informatics.

Shape "break-and-repair" strategy and its application to automated medical image segmentation.

In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed "break-and-repair" is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape "break-and-repair" strategy in medical image segmentation.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard.

PURPOSE: The authors examine potential bias when using a reference reader panel as "gold standard" for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy. METHODS: A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range of operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols. RESULTS: In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value < 0.0001) than LCA (ARB--2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27). CONCLUSIONS: Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.

Semiquantitative assessment of the microdistribution of fluorescence-labeled monoclonal antibody in small peritoneal disseminations of ovarian cancer.

Uniform antibody microdistribution throughout tumor nodules is crucial for antibody-targeted therapy, because non-uniform microdistribution leads to suboptimal therapeutic effect, a commonly observed limitation of therapeutic antibodies. Herein, we evaluated the microdistribution of different doses of intraperitoneally injected fluorescence-labeled full-antibody trastuzumab (15, 50, and 150 microg) and its Fab fragment (trastuzumab-Fab: 15 and 50 microg) in a mouse model of ovarian cancer with peritoneal disseminated tumor. A semiquantitative approach (central/peripheral accumulation ratio; C/P ratio) was developed using in situ fluorescence microscopy. Furthermore, we compared the microdistribution of intact trastuzumab with a mixed injection of trastuzumab and trastuzumab-Fab or serial injections of trastuzumab using in situ multicolor fluorescence microscopy. Fluorescence images after the administration of 15 or 50 microg trastuzumab and 15 microg trastuzumab-Fab demonstrated antibody accumulation in the tumor periphery, whereas administration of 150 microg trastuzumab and 50 microg trastuzumab-Fab showed relatively uniform accumulation throughout the tumor nodule. Using serial injections (19-h interval) of trastuzumab-rhodamine green and carboxytetramethylrhodamine (TAMRA), it was observed that the latterly injected trastuzumab-TAMRA was distributed more centrally than trastuzumab-rhodamine green injected first, whereas no difference was observed in the control mixed-injection group. Moreover, the mixed injection of trastuzumab and trastuzumab-Fab showed that trastuzumab-Fab distributed more centrally than the same amount of co-injected trastuzumab. Our results suggest that the strategies of increasing dose and using Fab fragments can be used to achieve a uniform antibody distribution within peritoneal disseminated nodules after intraperitoneal injection. Furthermore, serial-injection and mixed-injection strategies can modify antibody microdistribution within tumors and have the potential for preferential delivery of anticancer drugs to either the tumor periphery or its center.

Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Polyp enhancing level set evolution of colon wall: method and pilot study.

Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of colon cancer. In this work, we propose a colon wall evolution algorithm polyp enhancing level sets (PELS) based on the level-set formulation that regularizes and enhances polyps as a preprocessing step to CTC CAD algorithms. The underlying idea is to evolve the polyps towards spherical protrusions on the colon wall while keeping other structures, such as haustral folds, relatively unchanged and, thereby, potentially improve the performance of CTC CAD algorithms, especially for smaller polyps. To evaluate our methods, we conducted a pilot study using an arbitrarily chosen CTC CAD method, the surface normal overlap (SNO) CAD algorithm, on a nine patient CTC data set with 47 polyps of sizes ranging from 2.0 to 17.0 mm in diameter. PELS increased the maximum sensitivity by 8.1% (from 21/37 to 24/37) for small polyps of sizes ranging from 5.0 to 9.0 mm in diameter. This is accompanied by a statistically significant separation between small polyps and false positives. PELS did not change the CTC CAD performance significantly for larger polyps.

Optimization of a tomosynthesis system for the detection of lung nodules.

Mathematical observers that track human performance can be used to reduce the number of human observer studies needed to optimize imaging systems. The performance of human observers for the detection of a 3.6 mm lung nodule in anatomical backgrounds was measured as a function of varying tomosynthetic angle and compared with mathematical observers. The human observer results showed a dramatic increase in the percent of correct responses, from 80% in the projection images to 96% in the projection images with a tomosynthetic angle of just 3 degrees. This result suggests the potential usefulness of the scanned beam digital x-ray system for this application. Given the small number of images (40) used per tomosynthetic angle and the highly nonstationary statistical nature of the backgrounds, the nonprewhitening eye observer achieved a higher performance than the channelized Hotelling observer using a Laguerre-Gauss basis. The channelized Hotelling observer with internal noise and the eye filter matched to the projection data were shown to track human performance as the tomosynthetic angle changed. The validation of these mathematical observers extends their applicability to the optimization of tomosynthesis systems.

CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection.

PURPOSE: To retrospectively determine if three-dimensional (3D) viewing improves radiologists' accuracy in classifying true-positive (TP) and false-positive (FP) polyp candidates identified with computer-aided detection (CAD) and to determine candidate polyp features that are associated with classification accuracy, with known polyps serving as the reference standard. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained; this study was HIPAA compliant. Forty-seven computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42-76 years). Four radiologists classified 705 polyp candidates (53 TP candidates, 652 FP candidates) identified with CAD; initially, only two-dimensional images were used, but these were later supplemented with 3D rendering. Another radiologist unblinded to colonoscopy findings characterized the features of each candidate, assessed colon distention and preparation, and defined the true nature of FP candidates. Receiver operating characteristic curves were used to compare readers' performance, and repeated-measures analysis of variance was used to test features that affect interpretation. RESULTS: Use of 3D viewing improved classification accuracy for three readers and increased the area under the receiver operating characteristic curve to 0.96-0.97 (P<.001). For TP candidates, maximum polyp width (P=.038), polyp height (P=.019), and preparation (P=.004) significantly affected accuracy. For FP candidates, colonic segment (P=.007), attenuation (P<.001), surface smoothness (P<.001), distention (P=.034), preparation (P<.001), and true nature of candidate lesions (P<.001) significantly affected accuracy. CONCLUSION: Use of 3D viewing increases reader accuracy in the classification of polyp candidates identified with CAD. Polyp size and examination quality are significantly associated with accuracy.

Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.

PURPOSE: To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans. MATERIALS AND METHODS: The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader. RESULTS: The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05). CONCLUSION: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.

Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: pilot study.

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.

Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT.

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

Computed tomography colonography: feasibility of computer-aided polyp detection in a "first reader" paradigm.

OBJECTIVE: : To determine the feasibility of a computer-aided detection (CAD) algorithm as the "first reader" in computed tomography colonography (CTC). METHODS: : In phase 1 of a 2-part blind trial, we measured the performance of 3 radiologists reading 41 CTC studies without CAD. In phase 2, readers interpreted the same cases using a CAD list of 30 potential polyps. RESULTS: : Unassisted readers detected, on average, 63% of polyps > or =10 mm in diameter. Using CAD, the sensitivity was 74% (not statistically different). Per-patient analysis showed a trend toward increased sensitivity for polyps > or =10 mm in diameter, from 73% to 90% with CAD (not significant) without decreasing specificity. Computer-aided detection significantly decreased interobserver variability (P = 0.017). Average time to detection of the first polyp decreased significantly with CAD, whereas total reading case reading time was unchanged. CONCLUSION: : Computer-aided detection as a first reader in CTC was associated with similar per-polyp and per-patient detection sensitivity to unassisted reading. Computer-aided detection decreased interobserver variability and reduced the time required to detect the first polyp.

The histone deacetylase inhibitor MS-275 interacts synergistically with fludarabine to induce apoptosis in human leukemia cells.

Interactions between the novel benzamide histone deacetylase (HDAC) inhibitor MS-275 and fludarabine were examined in lymphoid and myeloid human leukemia cells in relation to mitochondrial injury, signal transduction events, and apoptosis. Prior exposure of Jurkat lymphoblastic leukemia cells to a marginally toxic concentration of MS-275 (e.g., 500 nM) for 24 h sharply increased mitochondrial injury, caspase activation, and apoptosis in response to a minimally toxic concentration of fludarabine (500 nM), resulting in highly synergistic antileukemic interactions and loss of clonogenic survival. Simultaneous exposure to MS-275 and fludarabine also led to synergistic effects, but these were not as pronounced as observed with sequential treatment. Similar interactions were noted in the case of (a) other human leukemia cell lines (e.g., U937, CCRF-CEM); (b) other HDAC inhibitors (e.g., sodium butyrate); and (c) other nucleoside analogues (e.g., 1-beta-D-arabinofuranosylcytosine, gemcitabine). Potentiation of fludarabine lethality by MS-275 was associated with acetylation of histones H3 and H4, down-regulation of the antiapoptotic proteins XIAP and Mcl-1, enhanced cytosolic release of proapoptotic mitochondrial proteins (e.g., cytochrome c, Smac/DIABLO, and apoptosis-inducing factor), and caspase activation. It was also accompanied by the caspase-dependent down-regulation of p27(KIP1), cyclins A, E, and D(1), and cleavage and diminished phosphorylation of retinoblastoma protein. However, increased lethality of the combination was not associated with enhanced fludarabine triphosphate formation or DNA incorporation and occurred despite a slight reduction in the S-phase fraction. Prior exposure to MS-275 attenuated fludarabine-mediated activation of MEK1/2, extracellular signal-regulated kinase, and Akt, and enhanced c-Jun NH(2)-terminal kinase phosphorylation; furthermore, inducible expression of constitutively active MEK1/2 or Akt significantly diminished MS-275/fludarabine-induced lethality. Combined exposure of cells to MS-275 and fludarabine was associated with a significant increase in generation of reactive oxygen species; moreover, both the increase in reactive oxygen species and apoptosis were largely attenuated by coadministration of the free radical scavenger L-N-acetylcysteine. Finally, prior administration of MS-275 markedly potentiated fludarabine-mediated generation of the proapoptotic lipid second messenger ceramide. Taken together, these findings indicate that the HDAC inhibitor MS-275 induces multiple perturbations in signal transduction, survival, and cell cycle regulatory pathways that lower the threshold for fludarabine-mediated mitochondrial injury and apoptosis in human leukemia cells. They also provide insights into possible mechanisms by which novel, clinically relevant HDAC inhibitors might be used to enhance the antileukemic activity of established nucleoside analogues such as fludarabine.