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Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes

Zhu, Y; Fearn, T; Chicken, DW; Austwick, MR; Somasundaram, SK; Mosse, CA; Clark, B; ... Bown, SG; + view all (2018) Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes. Journal of Biomedical Optics , 23 (8) , Article 085004. 10.1117/1.JBO.23.8.085004. Green open access

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Abstract

Sentinel lymph node biopsy is a standard diagnosis procedure to determine whether breast cancer has spread to the lymph glands in the armpit (the axillary nodes). The metastatic status of the sentinel node (the first node in the axillary chain that drains the affected breast) is the determining factor in surgery between conservative lumpectomy and more radical mastectomy including axillary node excision. The traditional assessment of the node requires sample preparation and pathologist interpretation. An automated elastic scattering spectroscopy (ESS) scanning device was constructed to take measurements from the entire cut surface of the excised sentinel node and to produce ESS images for cancer diagnosis. Here, we report on a partially supervised image classification scheme employing a Bayesian multivariate, finite mixture model with a Markov random field (MRF) spatial prior. A reduced dimensional space was applied to represent the scanning data of the node by a statistical image, in which normal, metastatic, and nonnodal-tissue pixels are identified. Our results show that our model enables rapid imaging of lymph nodes. It can be used to recognize nonnodal areas automatically at the same time as diagnosing sentinel node metastases with sensitivity and specificity of 85% and 94%, respectively. ESS images can help surgeons by providing a reliable and rapid intraoperative determination of sentinel nodal metastases in breast cancer.

Type: Article
Title: Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JBO.23.8.085004
Publisher version: https://doi.org/10.1117/1.JBO.23.8.085004
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian multivariate finite mixture model, Markov random field, discriminant dimension reduction, elastic scattering spectroscopy, image classification, principal component analysis, sentinel lymph nodes
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10054785
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