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Tissue classification of oncologic esophageal resectates based on hyperspectral data

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Esophageal carcinoma is the eighth most common cancer worldwide. Esophageal resection with gastric pull-up is a potentially curative therapeutic option. After this procedure, the specimen is examined by the pathologist to confirm complete removal of the cancer. An intraoperative analysis of the resectate would be less time-consuming and therefore improve patient safety.

Methods

Hyperspectral imaging (HSI) is a relatively new modality, which has shown promising results for the detection of tumors. Automatic approaches could support the surgeon in the visualization of tumor margins. Therefore, we evaluated four supervised classification algorithms: random forest, support vector machines (SVM), multilayer perceptron, and k-nearest neighbors to differentiate malignant from healthy tissue based on HSI recordings of esophago-gastric resectates in 11 patients.

Results

The best performances were obtained with a cancerous tissue detection of 63% sensitivity and 69% specificity with the SVM. In a leave-one patient-out cross-validation, the classification showed larger performance differences according to the patient data used. In less than 1 s, data classification and visualization was shown.

Conclusion

In this work, we successfully tested several classification algorithms for the automatic detection of esophageal carcinoma in resected tissue. A larger data set and a combination of several methods would probably increase the performance. Moreover, the implementation of software tools for intraoperative tumor boundary visualization will further support the surgeon during oncologic operations.

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Funding

This study was funded by the Federal Ministry of Education and Research 13GW0248B.

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Authors and Affiliations

  1. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany

    Marianne Maktabi, Hannes Köhler, Margarita Ivanova, Thomas Neumuth & Claire Chalopin

  2. Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany

    Boris Jansen-Winkeln, Jonathan Takoh, Stefan Niebisch, Sebastian M. Rabe & Ines Gockel

Authors
  1. Marianne Maktabi
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  2. Hannes Köhler
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  3. Margarita Ivanova
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  4. Boris Jansen-Winkeln
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  5. Jonathan Takoh
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  6. Stefan Niebisch
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  7. Sebastian M. Rabe
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  10. Claire Chalopin
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Correspondence to Marianne Maktabi.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Experimental hyperspectral measurements from patients for the evaluation of the new technology have obtained the ethics approval by the Ethics Committee of the University Hospital of Leipzig under 026/18-ek.

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Informed consent was obtained from all individual participants included in the study.

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Maktabi, M., Köhler, H., Ivanova, M. et al. Tissue classification of oncologic esophageal resectates based on hyperspectral data. Int J CARS 14, 1651–1661 (2019). https://doi.org/10.1007/s11548-019-02016-x

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  • DOI: https://doi.org/10.1007/s11548-019-02016-x

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