Please use this identifier to cite or link to this item:
doi:10.22028/D291-37750
Title: | Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy |
Author(s): | Kriegsmann, Mark Casadonte, Rita Maurer, Nadine Stoehr, Christine Erlmeier, Franziska Moch, Holger Junker, Kerstin Zgorzelski, Christiane Weichert, Wilko Schwamborn, Kristina Deininger, Sören-Oliver Gaida, Matthias Mechtersheimer, Gunhild Stenzinger, Albrecht Schirmacher, Peter Hartmann, Arndt Kriegsmann, Joerg Kriegsmann, Katharina |
Language: | English |
Title: | Journal of Cancer |
Volume: | 11 |
Issue: | 20 |
Pages: | 6081-6089 |
Publisher/Platform: | Ivyspring |
Year of Publication: | 2020 |
Free key words: | Oncocytic renal tumors chromophobe renal cell carcinoma renal oncocytoma mass spectrometry imaging proteomics |
DDC notations: | 610 Medicine and health |
Publikation type: | Journal Article |
Abstract: | Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO. |
DOI of the first publication: | 10.7150/jca.47698 |
URL of the first publication: | https://www.jcancer.org/v11p6081.htm |
Link to this record: | urn:nbn:de:bsz:291--ds-377507 hdl:20.500.11880/34137 http://dx.doi.org/10.22028/D291-37750 |
ISSN: | 1837-9664 |
Date of registration: | 27-Oct-2022 |
Description of the related object: | Supplementary Material |
Related object: | http://www.jcancer.org/v11p6081s1.pdf |
Faculty: | M - Medizinische Fakultät |
Department: | M - Urologie und Kinderurologie |
Professorship: | M - Prof. Dr. Michael Stöckle |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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File | Description | Size | Format | |
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v11p6081.pdf | 1,87 MB | Adobe PDF | View/Open |
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