Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI.
Simple Summary: For the effective treatment of lung cancer patients, correct tumor subtyping is of utmost importance, but it is often challenging in clinical routine. Using artificial intelligence and combining information from digital microscopy and matrix-assisted laser desorption/ionization mass...
| Publicado en: | Cancers Vol. 14; no. 24; pp. 6181 - 6202 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
12/15/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160958762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160958762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: 12/15/2022 vid: 14 iid: 24 pid: 97109 pub: MDPI artinfo: ui: 160958762 160958762 160958762 10.3390/cancers14246181 160958762 ppf: 6181 ppct: 21 formats: tig: atl: Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI. aug: au: Janßen, Charlotte Boskamp, Tobias Le'Clerc Arrastia, Jean Otero Baguer, Daniel Hauberg-Lotte, Lena Kriegsmann, Mark Kriegsmann, Katharina Steinbuß, Georg Casadonte, Rita Kriegsmann, Jörg Maaß, Peter affil: Center for Industrial Mathematics (ZeTeM), University of Bremen, 28359 Bremen, Germany sug: subj: Carcinoma, Non-Small-Cell Lung Diagnosis Carcinoma, Non-Small-Cell Lung Classification Deep Learning Neural Networks (Computer) Mass Spectrometry Methods Microscopy, Virtual Diagnosis, Computer Assisted Human Algorithms Adenocarcinoma of Lung Diagnosis Carcinoma, Squamous Cell Diagnosis Staining and Labeling Descriptive Statistics ab: Simple Summary: For the effective treatment of lung cancer patients, correct tumor subtyping is of utmost importance, but it is often challenging in clinical routine. Using artificial intelligence and combining information from digital microscopy and matrix-assisted laser desorption/ionization mass spectrometry imaging data have the potential to support the pathologist's decision-making process. We present a classification algorithm to distinguish between adenocarcinoma and squamous cell carcinoma of the lung based on the automatic detection of tumor areas in whole tissue sections and the determination of the tumor subtype with high accuracy. 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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