Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy

التفاصيل البيبلوغرافية
العنوان: Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy
المؤلفون: Kerstin Junker, Mark Kriegsmann, Holger Moch, Matthias M. Gaida, Peter Schirmacher, Christine Stoehr, Nadine Maurer, Rita Casadonte, Wilko Weichert, Gunhild Mechtersheimer, Arndt Hartmann, Franziska Erlmeier, Sören-Oliver Deininger, Albrecht Stenzinger, Joerg Kriegsmann, Kristina Schwamborn, Katharina Kriegsmann, Christiane Zgorzelski
المساهمون: University of Zurich, Kriegsmann, Mark, Kriegsmann, Katharina
المصدر: Journal of Cancer
بيانات النشر: Ivyspring International Publisher, 2020.
سنة النشر: 2020
مصطلحات موضوعية: 0301 basic medicine, Chromophobe Renal Cell Carcinoma, 610 Medicine & health, mass spectrometry imaging, Biology, Cross-validation, Mass spectrometry imaging, Oncocytic renal tumors, 03 medical and health sciences, 0302 clinical medicine, proteomics, 10049 Institute of Pathology and Molecular Pathology, medicine, Renal oncocytoma, chromophobe renal cell carcinoma, business.industry, medicine.disease, Linear discriminant analysis, Random forest, Support vector machine, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, 2730 Oncology, Differential diagnosis, Nuclear medicine, business, renal oncocytoma, Research Paper
الوصف: 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.
وصف الملف: Kriegsmann_JoC_2020.pdf - application/pdf
اللغة: English
تدمد: 1837-9664
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60bc3c3414e0cc8bdd35f7cc4bc7ba62Test
http://europepmc.org/articles/PMC7477404Test
حقوق: OPEN
رقم الانضمام: edsair.doi.dedup.....60bc3c3414e0cc8bdd35f7cc4bc7ba62
قاعدة البيانات: OpenAIRE