Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue

Author:

Guergan Selin1ORCID,Boeer Bettina1,Fugunt Regina1,Helms Gisela1,Roehm Carmen1,Solomianik Anna1ORCID,Neugebauer Alexander2,Nuessle Daniela2,Schuermann Mirjam2,Brunecker Kristin2,Jurjut Ovidiu2,Boehme Karen A.2ORCID,Dammeier Sascha2,Enderle Markus D.2,Bettio Sabrina3,Gonzalez-Menendez Irene3,Staebler Annette3,Brucker Sara Y.1ORCID,Kraemer Bernhard1ORCID,Wallwiener Diethelm1,Fend Falko3ORCID,Hahn Markus1

Affiliation:

1. Department of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, Germany

2. Erbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, Germany

3. Institute of Pathology and Neuropathology, Tuebingen University Hospital, 72076 Tübingen, Germany

Abstract

Breast conserving resection with free margins is the gold standard treatment for early breast cancer recommended by guidelines worldwide. Therefore, reliable discrimination between normal and malignant tissue at the resection margins is essential. In this study, normal and abnormal tissue samples from breast cancer patients were characterized ex vivo by optical emission spectroscopy (OES) based on ionized atoms and molecules generated during electrosurgical treatment. The aim of the study was to determine spectroscopic features which are typical for healthy and neoplastic breast tissue allowing for future real-time tissue differentiation and margin assessment during breast cancer surgery. A total of 972 spectra generated by electrosurgical sparking on normal and abnormal tissue were used for support vector classifier (SVC) training. Specific spectroscopic features were selected for the classification of tissues in the included breast cancer patients. The average classification accuracy for all patients was 96.9%. Normal and abnormal breast tissue could be differentiated with a mean sensitivity of 94.8%, a specificity of 99.0%, a positive predictive value (PPV) of 99.1% and a negative predictive value (NPV) of 96.1%. For 66.6% patients all classifications reached 100%. Based on this convincing data, a future clinical application of OES-based tissue differentiation in breast cancer surgery seems to be feasible.

Funder

Innovationsprogramm Baden-Württemberg

Open Access Publishing Fund of the University of Tuebingen

Publisher

MDPI AG

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