Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks
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 tech...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/3/686 |
| _version_ | 1797625069324730368 |
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| author | Frederic Kanter Jan Lellmann Herbert Thiele Steve Kalloger David F. Schaeffer Axel Wellmann Oliver Klein |
| author_facet | Frederic Kanter Jan Lellmann Herbert Thiele Steve Kalloger David F. Schaeffer Axel Wellmann Oliver Klein |
| author_sort | Frederic Kanter |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:51:36Z |
| format | Article |
| id | doaj.art-f3fb376631354e7ebaee15208bb0bc54 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T09:51:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-f3fb376631354e7ebaee15208bb0bc542023-11-16T16:15:45ZengMDPI AGCancers2072-66942023-01-0115368610.3390/cancers15030686Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural NetworksFrederic Kanter0Jan Lellmann1Herbert Thiele2Steve Kalloger3David F. Schaeffer4Axel Wellmann5Oliver Klein6Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, GermanyInstitute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, GermanyFraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, GermanyDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaInstitute of Pathology, Wittinger Strasse 14, 29223 Celle, GermanyBIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, GermanyDespite 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.https://www.mdpi.com/2072-6694/15/3/686MALDI mass spectrometry imagingpancreatic ductal adenocarcinomaneural-network models |
| spellingShingle | Frederic Kanter Jan Lellmann Herbert Thiele Steve Kalloger David F. Schaeffer Axel Wellmann Oliver Klein Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks Cancers MALDI mass spectrometry imaging pancreatic ductal adenocarcinoma neural-network models |
| title | Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks |
| title_full | Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks |
| title_fullStr | Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks |
| title_full_unstemmed | Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks |
| title_short | Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks |
| title_sort | classification of pancreatic ductal adenocarcinoma using maldi mass spectrometry imaging combined with neural networks |
| topic | MALDI mass spectrometry imaging pancreatic ductal adenocarcinoma neural-network models |
| url | https://www.mdpi.com/2072-6694/15/3/686 |
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