Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue
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 fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/14/3/338 |
| _version_ | 1797318868780187648 |
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| author | Selin Guergan Bettina Boeer Regina Fugunt Gisela Helms Carmen Roehm Anna Solomianik Alexander Neugebauer Daniela Nuessle Mirjam Schuermann Kristin Brunecker Ovidiu Jurjut Karen A. Boehme Sascha Dammeier Markus D. Enderle Sabrina Bettio Irene Gonzalez-Menendez Annette Staebler Sara Y. Brucker Bernhard Kraemer Diethelm Wallwiener Falko Fend Markus Hahn |
| author_facet | Selin Guergan Bettina Boeer Regina Fugunt Gisela Helms Carmen Roehm Anna Solomianik Alexander Neugebauer Daniela Nuessle Mirjam Schuermann Kristin Brunecker Ovidiu Jurjut Karen A. Boehme Sascha Dammeier Markus D. Enderle Sabrina Bettio Irene Gonzalez-Menendez Annette Staebler Sara Y. Brucker Bernhard Kraemer Diethelm Wallwiener Falko Fend Markus Hahn |
| author_sort | Selin Guergan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:58:39Z |
| format | Article |
| id | doaj.art-67d382e6de984874b27c584118bc7429 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-08T03:58:39Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-67d382e6de984874b27c584118bc74292024-02-09T15:10:17ZengMDPI AGDiagnostics2075-44182024-02-0114333810.3390/diagnostics14030338Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast TissueSelin Guergan0Bettina Boeer1Regina Fugunt2Gisela Helms3Carmen Roehm4Anna Solomianik5Alexander Neugebauer6Daniela Nuessle7Mirjam Schuermann8Kristin Brunecker9Ovidiu Jurjut10Karen A. Boehme11Sascha Dammeier12Markus D. Enderle13Sabrina Bettio14Irene Gonzalez-Menendez15Annette Staebler16Sara Y. Brucker17Bernhard Kraemer18Diethelm Wallwiener19Falko Fend20Markus Hahn21Department of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyErbe Elektromedizin GmbH, Waldhoernlestr. 17, 72072 Tübingen, GermanyInstitute of Pathology and Neuropathology, Tuebingen University Hospital, 72076 Tübingen, GermanyInstitute of Pathology and Neuropathology, Tuebingen University Hospital, 72076 Tübingen, GermanyInstitute of Pathology and Neuropathology, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyInstitute of Pathology and Neuropathology, Tuebingen University Hospital, 72076 Tübingen, GermanyDepartment of Women’s Health, Tuebingen University Hospital, 72076 Tübingen, GermanyBreast 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.https://www.mdpi.com/2075-4418/14/3/338optical emission spectroscopybreast cancertumor tissuetumor marginmachine learningsupport vector machine |
| spellingShingle | Selin Guergan Bettina Boeer Regina Fugunt Gisela Helms Carmen Roehm Anna Solomianik Alexander Neugebauer Daniela Nuessle Mirjam Schuermann Kristin Brunecker Ovidiu Jurjut Karen A. Boehme Sascha Dammeier Markus D. Enderle Sabrina Bettio Irene Gonzalez-Menendez Annette Staebler Sara Y. Brucker Bernhard Kraemer Diethelm Wallwiener Falko Fend Markus Hahn Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue Diagnostics optical emission spectroscopy breast cancer tumor tissue tumor margin machine learning support vector machine |
| title | Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue |
| title_full | Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue |
| title_fullStr | Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue |
| title_full_unstemmed | Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue |
| title_short | Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue |
| title_sort | optical emission spectroscopy for the real time identification of malignant breast tissue |
| topic | optical emission spectroscopy breast cancer tumor tissue tumor margin machine learning support vector machine |
| url | https://www.mdpi.com/2075-4418/14/3/338 |
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