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      Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set


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          Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group ( P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to ≤5 cm) as with the entire data set ( P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

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          The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

          The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
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            Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists.

            Breast cancer is a heterogeneous disease encompassing a variety of entities with distinct morphological features and clinical behaviors. Although morphology is often associated with the pattern of molecular aberrations in breast cancers, it is also clear that tumors of the same histological type show remarkably different clinical behavior. This is particularly true for 'basal-like cancer', which is an entity defined using gene expression analysis. The purpose of this article was to review the current state of knowledge of basal-like breast cancers, to discuss the relationship between basal-like and triple-negative breast cancers, and to clarify practical implications of these diagnoses for pathologists and oncologists.
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              TCGA-assembler: open-source software for retrieving and processing TCGA data.


                Author and article information

                NPJ Breast Cancer
                NPJ Breast Cancer
                NPJ breast cancer
                11 September 2016
                11 May 2016
                14 November 2016
                : 2
                : 16012
                [1 ]Department of Radiology, The University of Chicago, Chicago, IL, USA
                [2 ]Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL, USA
                [3 ]Department of Radiology, University of Wisconsin—Madison, Madison, WI, USA
                [4 ]National Cancer Institute, Cancer Imaging Program, Bethesda, MA, USA
                [5 ]Department of Genetics, University of North Carolina—Chapel Hill, Chapel Hill, NC, USA
                [6 ]Department of Medicine, The University of Chicago, Chicago, IL, USA
                [7 ]Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
                [8 ]Department of Radiology, University of Miami Health System, Miami, FL, USA
                [9 ]Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [10 ]Department of Radiology, MD Anderson Cancer Center, Houston, TX, USA
                [11 ]Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
                Author notes
                Correspondence: ML Giger ( m-giger@ 123456uchicago.edu ) or Y Ji ( jiyuan@ 123456uchicago.edu )

                These authors contributed equally to this work.


                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/



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