Publication Details

Category Text Publication
Reference Category Journals
DOI 10.3390/cancers14174342
Licence creative commons licence
Title (Primary) Multi-class cancer subtyping in salivary gland carcinomas with MALDI imaging and deep learning
Author Pertzborn, D.; Arolt, C.; Ernst, G.; Lechtenfeld, O.J. ORCID logo ; Kaesler, J.; Pelzel, D.; Guntinas-Lichius, O.; von Eggeling, F.; Hoffmann, F.
Source Titel Cancers
Year 2022
Department ANA
Volume 14
Issue 17
Page From art. 4342
Language englisch
Topic T9 Healthy Planet
Keywords MALDI imaging; deep learning; salivary gland carcinomas; explainable artificial intelligence
UFZ wide themes 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.
Persistent UFZ Identifier
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