Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.3390/cancers14174342
Lizenz creative commons licence
Titel (primär) Multi-class cancer subtyping in salivary gland carcinomas with MALDI imaging and deep learning
Autor Pertzborn, D.; Arolt, C.; Ernst, G.; Lechtenfeld, O.J. ORCID logo ; Kaesler, J.; Pelzel, D.; Guntinas-Lichius, O.; von Eggeling, F.; Hoffmann, F.
Quelle Cancers
Erscheinungsjahr 2022
Department ANA
Band/Volume 14
Heft 17
Seite von art. 4342
Sprache englisch
Topic T9 Healthy Planet
Keywords MALDI imaging; deep learning; salivary gland carcinomas; explainable artificial intelligence
UFZ Querschnittsthemen ProVIS;
Abstract Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.
dauerhafte UFZ-Verlinkung
Pertzborn, D., Arolt, C., Ernst, G., Lechtenfeld, O.J., Kaesler, J., Pelzel, D., Guntinas-Lichius, O., von Eggeling, F., Hoffmann, F. (2022):
Multi-class cancer subtyping in salivary gland carcinomas with MALDI imaging and deep learning
Cancers 14 (17), art. 4342 10.3390/cancers14174342