Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Frederic Kanter*, Jan Lellmann, Herbert Thiele, Steve Kalloger, David F. Schaeffer, Axel Wellmann, Oliver Klein

*Korrespondierende/r Autor/-in für diese Arbeit


Simple Summary:
Pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all pancreatic malignancies, and has a generally poor prognosis. Although it is the most common neoplastic disease of the pancreas, differential diagnosis is hindered by the lack of accurate and reliable diagnostic assays. Identification of molecular signatures for PDAC diagnosis offers a solution to improve the clinical and patient management. However, comprehensive omics profiling is time- and relatively cost-intensive and limited by tissue heterogeneity. Thus, it is not implemented in clinical routine. In this study, we investigate the feasibility of MALDI-MSI in combination with a neural-network-based analysis for accurate classification of pancreatic-ductal-adenocarcinoma patients. We provide evidence of the usefulness of this technology to support PDAC assessment which is promising for pathological aid.

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
PublikationsstatusVeröffentlicht - 02.2023