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
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:
Link to this record: urn:nbn:de:bsz:291--ds-377507
ISSN: 1837-9664
Date of registration: 27-Oct-2022
Description of the related object: Supplementary Material
Related object:
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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