High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Sreedhar, Hari; Varma, Vishal K.; Nguyen, Phong; Davidson, Bennett; Akkina, Sanjeev; Guzman, Grace; Setty, Suman; Kajdacsy-Balla, André; Walsh, Michael J.

doi:10.3791/52332-v

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Histopathology is the gold standard for cancer diagnosis, but it is invasive and prone to interobserver variability. 85,86 The kappa values between two pathologists ranged from 0.59 to 0.69 in tumour pathological diagnosis. [87][88][89] OS is able to permit non-invasive and objective analysis of biochemical and subcellular alterations of tissues, and the results are well correlated with histopathology.…”

Section: Os Diagnosis Based On Tissuesmentioning

confidence: 99%

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

2017

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Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop noninvasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

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Section: Os Diagnosis Based On Tissuesmentioning

confidence: 99%

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

2017

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“…Based on the molecular composition of the sample, different frequencies of the incident IR light will be absorbed. Features of the resulting IR spectrum, which corresponds to specific molecular features such as amides, phosphates, and methyl groups, can then be selected to generate a 2D image [33].…”

Section: Overview Of Techniquesmentioning

confidence: 99%

Advances in multimodal mass spectrometry for single‐cell analysis and imaging enhancement

Croslow,

Trinklein,

Sweedler

2024

FEBS Letters

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Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe‐based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single‐cell and imaging analyses.

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“…Mid-infrared (MIR) microscopic imaging is a modern analytical method that is widely used to characterize the components of biological specimens [1][2][3] and has already been applied in tissue histology [4][5][6]. MIR microscopic imaging was, for example, used to aid in the differentiation between benign and malignant disease [3,5,[7][8][9][10][11] and was tested for imaging lymph node histopathology [6,12]. Our group successfully implemented this method for the differentiation between reactive lymph nodes, small and large cell lymphoma using follicular lymphoma (FL), and diffuse large B-cell lymphoma (DLBCL) as an example [13].…”

Section: Introductionmentioning

confidence: 99%

Deep learning analysis of mid‐infrared microscopic imaging data for the diagnosis and classification of human lymphomas

Zelger

Brunner

Zelger

et al. 2023

Journal of Biophotonics

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The present study presents an alternative analytical workflow that combines mid‐infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm−1 to 850 cm−1. The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.This article is protected by copyright. All rights reserved.

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High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Cited by 5 publications

References 36 publications

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Advances in multimodal mass spectrometry for single‐cell analysis and imaging enhancement

Deep learning analysis of mid‐infrared microscopic imaging data for the diagnosis and classification of human lymphomas

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