ArticleProceedings of the National Academy of Sciences of the United States of America2018
Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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72 citing papers in PubMed, 140 citations in OpenAlex.
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- Ultrasensitive in vivo infrared spectroscopic imaging via oblique photothermal microscopy.Nature communications · 2025Article
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- Label-free Aβ plaque detection in Alzheimer's disease brain tissue using infrared microscopy and neural networks.Heliyon · 2025Article
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- Benchtop IR Imaging of Live Cells: Monitoring the Total Mass of Biomolecules in Single Cells.Analytical chemistry · 2024Article
- Exploiting Matrix Stiffness to Overcome Drug Resistance.ACS biomaterials science & engineering · 2024Review
- Overtone photothermal microscopy for high-resolution and high-sensitivity vibrational imaging.Nature communications · 2024Article
- INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation.Machine learning with applications · 2024Article
- Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data.Journal of personalized medicine · 2024Article
- Structural characterization of amyloid aggregates with spatially resolved infrared spectroscopy.Methods in enzymology · 2024Article
- Intermediate Antiparallel β Structure in Amyloid β Plaques Revealed by Infrared Spectroscopic Imaging.ACS chemical neuroscience · 2023Article
- Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning.Cancer research communications · 2023Article
- Infrared spectroscopic laser scanning confocal microscopy for whole-slide chemical imaging.Nature communications · 2023Article
12 more citing papers are in PubMed but not listed here.
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6 authors at 2 institutions in 1 country.
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Abstract
Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients' caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.