Multimodal lung cancer subtyping using deep learning neural networks on whole slide tissue images and MALDI MSI
Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an ef...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14 December 2022
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| In: |
Cancers
Year: 2022, Jahrgang: 14, Heft: 24, Pages: 1-21 |
| ISSN: | 2072-6694 |
| DOI: | 10.3390/cancers14246181 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/cancers14246181 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2072-6694/14/24/6181 
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| Verfasserangaben: | Charlotte Janßen, Tobias Boskamp, Jean Le’Clerc Arrastia, Daniel Otero Baguer, Lena Hauberg-Lotte, Mark Kriegsmann, Katharina Kriegsmann, Georg Steinbuß, Rita Casadonte, Jörg Kriegsmann and Peter Maaß |
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| 520 | |a Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue. | ||
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| 650 | 4 | |a lung cancer | |
| 650 | 4 | |a mass spectrometry imaging | |
| 650 | 4 | |a non-small cell lung cancer | |
| 650 | 4 | |a tumor detection | |
| 650 | 4 | |a tumor segmentation | |
| 650 | 4 | |a whole slide images | |
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| 700 | 1 | |a Le’Clerc Arrastia, Jean |e VerfasserIn |4 aut | |
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