Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

Beuque, Manon; Martin‐Lorenzo, Marta; Balluff, Benjamin; Woodruff, Henry C.; Lucas, Marit; Bruin, Daniël M. de; Timmeren, Janita E. van; Boer, Onno J. de; Heeren, Ron M. A.; Meijer, Sybren L.; Lambin, Philippe

doi:10.1016/j.compbiomed.2021.104918

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Moreover, we further integrate ″.mzXML″ and ″.imzML″ into MMP in addition to the ″.raw” format. As shown in Figure S2, the results obtained from MMP matched with the data generated by different MSI analysis platforms (MALDI/AP-SMALDI/SIMS/DESI). , …”

Section: Resultssupporting

confidence: 62%

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research

Bi,

Wang,

et al. 2023

Anal. Chem.

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Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.

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Section: Resultssupporting

confidence: 62%

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research

Bi,

Wang,

et al. 2023

Anal. Chem.

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“…Thus, FLIM can directly take advantage of already existing fluorescence datasets to boost computer-vision model performance. This direct compatibility with standard fluorescence may provide FLIM with advantages over other imaging techniques, such as FT-IR ( 51 ), Raman ( 52 ), and mass spectrometry ( 53 ). Since immunofluorescence is already an established histopathological technique ( 54–56 ), adoption of FLIM for computational histopathology might not require as much groundwork as these techniques.…”

Section: Discussionmentioning

confidence: 99%

Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues

Smolen

Wooley

2022

PNAS Nexus

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Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy image modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by FLIM. This accuracy is higher than what would be achieved with regular single or dual-channel fluorescence images under the same settings, particularly for CNNs pretrained on publicly available fluorescent cell or general photographic datasets. Additionally, generated FLIM images could be predicted from just the fluorescence image data by using a dense U-Net CNN model trained on a subset of ground-truth FLIM images. These U-Net CNN generated FLIM images demonstrated high similarity to ground truth and improved accuracy in cell detection and classification over fluorescence alone when used as input to a variety of commonly used CNNs. This improved accuracy was maintained even when the FLIM images were generated by a U-Net CNN trained on only a few example FLIM images.

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“…Data from other modalities provide complementary molecular or spatial information, which may be used to enhance the quality of MSI data or provide additional insights using more targeted analytical tools. MSI data have been coupled with histology, [ 91 , 133 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 ] fluorescence microscopy, [ 188 , 189 , 190 , 191 ] Allen brain atlas, [ 107 , 192 ] topology, [ 193 , 194 , 195 ] electron microscopy, [ 196 , 197 ] Raman spectroscopy, [ 198 , 199 , 200 , 201 ] infrared spectroscopy, [ 202 ] magnetic resonance imaging, [ 93 , 203 ] and microsampling LC‐MS/MS analysis of proteins, transcripts, and genes. [ 164 , 204 , 205 , 206 , 207 , 208 , 209 , 210 ] In these studies, computational approaches have been used for registration of the multimodal data.…”

Section: Computational Methods For Msi Data Analysismentioning

confidence: 99%

Emerging Computational Methods in Mass Spectrometry Imaging

Laskin

2022

Advanced Science

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Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

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Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

Cited by 15 publications

References 22 publications

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research

Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues

Emerging Computational Methods in Mass Spectrometry Imaging

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