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      Evaluating feasibility of an automated 3-dimensional scanner using Raman spectroscopy for intraoperative breast margin assessment

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          Abstract

          Breast conserving surgery is the preferred treatment for women diagnosed with early stage invasive breast cancer. To ensure successful breast conserving surgeries, efficient tumour margin resection is required for minimizing tumour recurrence. Currently surgeons rely on touch preparation cytology or frozen section analysis to assess tumour margin status intraoperatively. These techniques have suboptimal accuracy and are time-consuming. Tumour margin status is eventually confirmed using postoperative histopathology that takes several days. Thus, there is a need for a real-time, accurate, automated guidance tool that can be used during tumour resection intraoperatively to assure complete tumour removal in a single procedure. In this paper, we evaluate feasibility of a 3-dimensional scanner that relies on Raman Spectroscopy to assess the entire margins of a resected specimen within clinically feasible time. We initially tested this device on a phantom sample that simulated positive tumour margins. This device first scans the margins of the sample and then depicts the margin status in relation to an automatically reconstructed image of the phantom sample. The device was further investigated on breast tissues excised from prophylactic mastectomy specimens. Our findings demonstrate immense potential of this device for automated breast tumour margin assessment to minimise repeat invasive surgeries.

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          Twenty-Year Follow-up of a Randomized Trial Comparing Total Mastectomy, Lumpectomy, and Lumpectomy plus Irradiation for the Treatment of Invasive Breast Cancer

          New England Journal of Medicine, 347(16), 1233-1241
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            Sparse multinomial logistic regression: fast algorithms and generalization bounds.

            Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency of the proposed methods.
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              Variability in reexcision following breast conservation surgery.

              Health care reform calls for increasing physician accountability and transparency of outcomes. Partial mastectomy is the most commonly performed procedure for invasive breast cancer and often requires reexcision. Variability in reexcision might be reflective of the quality of care. To assess hospital and surgeon-specific variation in reexcision rates following partial mastectomy. An observational study of breast surgery performed between 2003 and 2008 intended to evaluate variability in breast cancer surgical care outcomes and evaluate potential quality measures of breast cancer surgery. Women with invasive breast cancer undergoing partial mastectomy from 4 institutions were studied (1 university hospital [University of Vermont] and 3 large health plans [Kaiser Permanente Colorado, Group Health, and Marshfield Clinic]). Data were obtained from electronic medical records and chart abstraction of surgical, pathology, radiology, and outpatient records, including detailed surgical margin status. Logistic regression including surgeon-level random effects was used to identify predictors of reexcision. Incidence of reexcision. A total of 2206 women with 2220 invasive breast cancers underwent partial mastectomy and 509 patients (22.9%; 95% CI, 21.2%-24.7%) underwent reexcision (454 patients [89.2%; 95% CI, 86.5%-91.9%] had 1 reexcision, 48 [9.4%; 95% CI, 6.9%-12.0%] had 2 reexcisions, and 7 [1.4%; 95% CI, 0.4%-2.4%] had 3 reexcisions). Among all patients undergoing initial partial mastectomy, total mastectomy was performed in 190 patients (8.5%; 95% CI, 7.2%-9.5%). Reexcision rates for margin status following initial surgery were 85.9% (95% CI, 82.0%-89.8%) for initial positive margins, 47.9% (95% CI, 42.0%-53.9%) for less than 1.0 mm margins, 20.2% (95% CI, 15.3%-25.0%) for 1.0 to 1.9 mm margins, and 6.3% (95% CI, 3.2%-9.3%) for 2.0 to 2.9 mm margins. For patients with negative margins, reexcision rates varied widely among surgeons (range, 0%-70%; P = .003) and institutions (range, 1.7%-20.9%; P < .001). Reexcision rates were not associated with surgeon procedure volume after adjusting for case mix (P = .92). Substantial surgeon and institutional variation were observed in reexcision following partial mastectomy in women with invasive breast cancer.
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                Author and article information

                Contributors
                anita.mahadevan-jansen@vanderbilt.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 October 2017
                19 October 2017
                2017
                : 7
                : 13548
                Affiliations
                [1 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Vanderbilt Biophotonics Center, Vanderbilt University, ; Nashville, TN 37235 USA
                [2 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Department of Biomedical Engineering, Vanderbilt University, ; Nashville, TN 37235 USA
                [3 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Department of Biomedical Engineering, Northwestern University, ; Evanston, IL 60208 USA
                [4 ]ISNI 0000 0004 0534 4718, GRID grid.418158.1, Genentech, ; San Francisco, CA 94080 USA
                [5 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Division of Pathology, Vanderbilt University Medical Center, ; Nashville, TN 37232 USA
                [6 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, Division of Surgical Oncology, Vanderbilt University Medical Center, ; Nashville, TN 37232 USA
                Author information
                http://orcid.org/0000-0002-8333-9995
                http://orcid.org/0000-0002-5188-1931
                Article
                13237
                10.1038/s41598-017-13237-y
                5648832
                29051521
                e8a8467e-189d-4829-8a30-cb260290300b
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 May 2017
                : 20 September 2017
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