All Publications

  • Rim Sign in Breast Lesions on Diffusion-Weighted Magnetic Resonance Imaging: Diagnostic Accuracy and Clinical Usefulness JOURNAL OF MAGNETIC RESONANCE IMAGING Kang, B. J., Lipson, J. A., Planey, K. R., Zackrisson, S., Ikeda, D. M., Kao, J., Pal, S., Moran, C. J., Daniel, B. L. 2015; 41 (3): 616-623


    To investigate the diagnostic accuracy and clinical usefulness of the rim sign in breast lesions observed in diffusion-weighted magnetic resonance imaging (DWI).The magnetic resonance imaging (MRI) findings of 98 pathologically confirmed lesions (62 malignant and 36 benign) in 84 patients were included. Five breast radiologists were asked to independently review the breast MRI results, to grade the degree of high peripheral signal, the "rim sign," in the DWI, and to confirm the mean apparent diffusion coefficient (ADCmean ) values. We analyzed the diagnostic accuracy and compared the consensus (when ≥4 of 5 independent reviewers agreed) results of the rim sign with the ADCmean values. Additionally, we evaluated the correlation between the dynamic contrast-enhanced (DCE)-MRI morphologic appearance and DWI rim sign.According to the consensus results, the rim sign in DWI was observed on 59.7% of malignant lesions and 19.4% of benign lesions. The sensitivity, specificity, and area under the curve (AUC) value for the rim sign in DWI were 59.7%, 80.6%, and 0.701, respectively. The sensitivity, specificity, and AUC value for the ADCmean value (criteria ≤1.46 × 10(-3) mm(2) /sec) were 82.3%, 63.9%, and 0.731, respectively. Based on consensus, no correlation was observed between the DCE-MRI and DWI rim signs.In DWI, a high-signal rim is a valuable morphological feature for improving specificity in DWI.J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc.

    View details for DOI 10.1002/jmri.24617

    View details for Web of Science ID 000349967700006

    View details for PubMedID 24585455

  • Database integration of 4923 publicly-available samples of breast cancer molecular and clinical data. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science Planey, C. R., Butte, A. J. 2013; 2013: 138-142


    We outline a paradigm for meta-microarray database creation and integration with clinical variables. We use as our implementation example a breast cancer database linking RNA expression measurements (by microarray) and clinical variables, such as survival metrics and tumor size. Such an endeavor involves integrating across different microarray datasets as well as clinical parameters. To this end, we created a data curation and processing pipeline, formal database ontology, and SQL schema to optimally query, analyze and visualize data from over 30 publicly available breast cancer microarray studies listed in the Gene Expression Omnibus (GEO). We demonstrate several pilot examples using this database. This methodology serves as a model for future meta-analyses of complex public clinical datasets, in particular those in the field of cancer.

    View details for PubMedID 24303324

  • Temporal Sampling Requirements for Reference Region Modeling of DCE-MRI Data in Human Breast Cancer JOURNAL OF MAGNETIC RESONANCE IMAGING Planey, C. R., Welch, E. B., Xu, L., Chakravarthy, A. B., Gatenby, J. C., Freehardt, D., Mayer, I., Meszeoly, I., Kelley, M., Means-Powell, J., Gore, J. C., Yankeelov, T. E. 2009; 30 (1): 121-134


    To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer.Simulations were used to study errors in pharmacokinetic parameters (K(trans) and v(e)) estimated by the RR model using six DCE-MRI acquisitions over a range of pharmacokinetic parameter values, arterial input functions, and temporal samplings. DCE-MRI data were acquired on 12 breast cancer patients and parameters were estimated using the native resolution data (16.4 seconds) and compared to downsampled 32.8-second and 65.6-second data.Simulations show that, in the majority of parameter combinations, the RR model results in an error less than 20% in the extracted parameters with temporal sampling as poor as 35.6 seconds. The experimental results show a high correlation between K(trans) and v(e) estimates from data acquired at 16.4-second temporal resolution compared to the downsampled 32.8-second data: the slope of the regression line was 1.025 (95% confidence interval [CI]: 1.021, 1.029), Pearson's correlation r = 0.943 (95% CI: 0.940, 0.945) for K(trans), and 1.023 (95% CI: 1.021. 1.025), r = 0.979 (95% CI: 0.978, 0.980) for v(e). For the 64-second temporal resolution data the results were: 0.890 (95% CI: 0.894, 0.905), r = 0.8645, (95% CI: 0.858, 0.871) for K(trans), and 1.041 (95% CI: 1.039, 1.043), r = 0.970 (95% CI: 0.968, 0.971) for v(e).RR analysis allows for a significant reduction in temporal sampling requirements and this lends itself to analyze DCE-MRI data acquired in practical situations.

    View details for DOI 10.1002/jmri.21812

    View details for Web of Science ID 000267452600016

    View details for PubMedID 19557727

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