The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure

Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers

This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The ensemble of classifiers (which can be considered as a computer panel of experts) uses 64 image features of the nodules across four categories (shape, intensity, texture, and size) to predict semantic characteristics. The active learning begins the training phase with nodules on which radiologists’ semantic ratings agree, and incrementally learns how to classify nodules on which the radiologists do not agree. Using our proposed approach, the classification accuracy of the ensemble of classifiers is higher than the accuracy of a single classifier. In the long run, our proposed approach can be used to increase consistency among radiological interpretations by providing physicians a “second read”

Imaging in pleural mesothelioma: A review of the 11th International Conference of the International Mesothelioma Interest Group

Imaging of malignant pleural mesothelioma (MPM) is essential to the diagnosis, assessment, and monitoring of this disease. The complex morphology and growth pattern of MPM, however, create unique challenges for image acquisition and interpretation. These challenges have captured the attention of investigators around the world, some of whom presented their work at the 2012 International Conference of the International Mesothelioma Interest Group (iMig 2012) in Boston, Massachusetts, USA, September 2012. The measurement of tumor thickness on computed tomography (CT) scans is the current standard of care in the assessment of MPM tumor response to therapy; in this context, variability among observers in the measurement task and in the tumor response classification categories derived from such measurements was reported. Alternate CT-based tumor response criteria, specifically direct measurement of tumor volume change and change in lung volume as a surrogate for tumor response, were presented. Dynamic contrast-enhanced CT has a role in other settings, but investigation into its potential use for imaging mesothelioma tumor perfusion only recently has been initiated. Magnetic resonance imaging (MRI) and positron-emission tomography (PET) are important imaging modalities in MPM and complement the information provided by CT. The pointillism sign in diffusion-weighted MRI was reported as a potential parameter for the classification of pleural lesions as benign or malignant, and PET parameters that measure tumor activity and functional tumor volume were presented as indicators of patient prognosis. Also reported was the use of PET/CT in the management of patients who undergo high-dose radiation therapy. Imaging for MPM impacts everything from initial patient diagnosis to the outcomes of clinical trials; iMig 2012 captured this broad range of imaging applications as investigators exploit technology and implement multidisciplinary approaches toward the benefit of MPM patients. (C) 2013 Elsevier Ireland Ltd. All rights reserved

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