Y Jiang

University of Chicago, Chicago, Illinois, United States

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Publications (9)29.78 Total impact

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    ABSTRACT: To evaluate whether focal liver lesions (FLLs) exhibit a homogeneous appearance on apparent diffusion coefficient (ADC) maps and whether there is inter-section variation in the calculated ADC values of FLLs (inter-section range). Eighty-eight patients with 128 FLLs (70 benign, 58 malignant) who underwent abdominal magnetic resonance imaging (MRI) including diffusion-weighted (DW)-MRI were included. Two observers evaluated variation of signal intensity of each FLL within each ADC map image (intra-section) and among different ADC map images through the lesion (inter-section). ADC values of each FLL and neighbouring liver parenchyma were measured on all sections. The inter-section range of FLLs was compared with the neighbouring liver parenchyma. Intra-section inhomogeneity was noted in 39.8% (97/244 sections) and 38.9% (95/244) of benign lesions, and 61% (114/187 sections) and 61.5% (115/187) of malignant lesions, by observer 1 and observer 2, respectively. Inter-section inhomogeneity was noted in 25.7% (18/70) and 27.1% (19/70) of benign lesions, and 51.7% (30/58) and 50% (29/58) of malignant lesions, by observer 1 and observer 2, respectively. The inter-section range for both benign (0.28 × 10(-3) mm²/s) and malignant (0.25 × 10(-3) mm²/s) FLLs were significantly greater than that of liver parenchyma surrounding benign (0.16 × 10(-3) mm²/s, p < 0.001) and malignant (0.14 × 10(-3) mm²/s, p = 0.01) FLLs. Due to intra-/inter-section variations in ADC values of benign and malignant FLLs, a single ADC value may not reliably represent the entire lesion.
    No preview · Article · Mar 2014 · Clinical Radiology
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    ABSTRACT: Purpose: In this study we evaluate the short term reproducibility of Diffusion Weighted MR Imaging (DW‐MRI) of the prostate by analyzing variation in ADC maps. Methods: 14 patients with biopsy proven prostate cancer were evaluated under an IRB‐approved protocol. Each patient underwent two, identical DW‐MRI scans with the patient remaining on the table and only automatic shimming with tuning and matching between acquisitions. ADC maps were generated using a least squares fit. The prostate and ROIs within cancer lesions were delineated on each scan per patient by two radiologists using the b‐0 images and a rigid local registration was performed using multiple radiologist‐defined landmarks throughout the prostate on each of the two scans. The absolute and percentage differences in ADC per voxel were calculated. For each patient the prostate was divided into equally spaced sextants and the voxel‐based variation in each sextant was compared. Results: The absolute difference in ADC per voxel within the prostate ranged from 1.13×10−9 to 1.91×10∼−3 mm2/sec (mean and standard deviation percentage difference 10.35% ± 10.91%). The largest variation is seen in the posterior apex (mean 11.46%, median 8.02%, standard deviation 11.73%) although we were not able to demonstrate statistical significance in the difference in variation between sextants. Cancer ROIs showed a mean, median and standard deviation in ADC difference of 13.36%, 6.90% and 14.48%, respectively. Conclusion: DW‐MRI has strong potential to become a powerful quantitative imaging biomarker for prostate cancer. It is imperative to know the reproducibility of the imaging method when utilizing DW‐MRI in the clinic or developing new approaches for its use. Our data demonstrates that ADC variation within the prostate is modest, on the order of 10%, similar to other abdominal tissues, and the variation appears to be highest in the apex region of the prostate.
    No preview · Article · Jun 2013 · Medical Physics
  • M Giger · A Toledano · K Myers · Y Jiang
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    ABSTRACT: Receiver Operating Characteristic (ROC) analysis has been a mainstay of many research developments as well as various clinical studies/trials. It has provided medical physicists with a way to objectively measure how data are presented in an image, how people perceive those images, and how one can compare different observers or different imaging modalities with each other. ROC analysis plays an important role in both technology assessment and clinical decision‐making, especially as various aspects of imaging biomarkers and personalized medicine are evaluated. Over the past five years, on average, almost 40 papers/year that were published in MEDICAL PHYSICS utilized ROC analysis. The challenges and opportunities in ROC analysis research and in its application in various tasks are active areas, including expanding the mathematical formulation for multiple lesions per image, location‐based sensitivity, and evaluation without ground truth, as well as expanding its role in imaging biomarker validation, assessing response to therapy, theranostics, and image‐based phenotyping with genomics (image‐omics). Learning Objectives: 1. Review the mathematical foundations and implementation of ROC analysis in basic research and clinical trials; and understand the role and limitations of ROC analysis in large scale clinical studies/trials 2. Recognize advances in ROC analysis in order to incorporate multiple lesions per image, location‐based sensitivity, evaluation without ground truth, theranostics, and others 3. Appreciate the evolving role of ROC analysis in the evaluation of imaging biomarkers and image‐based phenotyping Research supported by NIH, DOE, and DOD. COI: Stockholder, Hologic, Inc Shareholder, Quantitative Insights, Inc Royalties, Hologic, Inc Royalties, General Electric Company Royalties, MEDIAN Technologies Royalties, Riverain Technologies, LLC Royalties, Mitsubishi Corporation Royalties, Toshiba Maryellen Giger Corporation Researcher, Koninklijke Philips Electronics NV Researcher, U‐Systems, Inc
    No preview · Article · Jun 2013 · Medical Physics

  • No preview · Article · Feb 2013 · Annals of the Rheumatic Diseases
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    M. R. Clark · M Giger · Y Jiang · V Liarski

    Full-text · Article · Sep 2012 · Arthritis Research & Therapy
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    ABSTRACT: Several of the authors have previously published an analysis of multiple sources of uncertainty in the receiver operating characteristic (ROC) assessment and comparison of diagnostic modalities. The analysis assumed that the components of variance were the same for the modalities under comparison. The purpose of the present work is to obtain a generalization that does not require that assumption. The generalization is achieved by splitting three of the six components of variance in the previous model into modality-dependent contributions. Two distinct formulations of this approach can be obtained from alternative choices of the three components to be split; however, a one-to-one relationship exists between the magnitudes of the components estimated from these two formulations. The method is applied to a study of multiple readers, with and without the aid of a computer-assist modality. performing the task of discriminating between benign and malignant clusters of microcalcifications. Analysis according to the first method of splitting shows large decreases in the reader and reader-by-case components of variance when the computer assist is used by the readers. Analysis in terms of the alternative splitting shows large decreases in the corresponding modality-interaction components. A solution to the problem of multivariate ROC analysis without the assumption of equal variance structure across modalities has been provided. Alternative formulations lead to consistent results related by a one-to-one mapping. A surprising result is that estimates of confidence intervals and numbers of cases and readers required for a specified confidence interval remain the same in the more general model as in the restricted model.
    No preview · Article · Aug 2001 · Academic Radiology
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    ABSTRACT: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer. One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications. Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03). Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.
    No preview · Article · Apr 1996 · Radiology
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    ABSTRACT: A computerized technique is being developed to automatically detect clustered microcalcifications on digital mammograms. The method consists of three steps. First the signal-to-noise ratio of microcalcifications is enhanced by filtering the image to reduce the normal background structure of the mammogram. Second, signals (potential microcalcifications) are identified by means of global grey-level thresholding, morphological erosion, and a local adaptive grey-level thresholding. Third, the number of falsely detected signals is reduced by examining the power spectrum of individual signals, determining the spatial distribution of the signals, and examining the relationship between size, shape, and background pixel value of microcalcifications. Using this approach, the computer scheme was tested using 78 mammograms, half containing subtle clusters of microcalcifications and half containing no clusters. The scheme was capable of detecting 87% of true clusters with, on average, two false clusters detected per image
    No preview · Conference Paper · Nov 1992

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