Identification of cancer associated molecular changes in histologically benign vulval disease found in association with vulval squamous cell carcinoma using Fourier transform infrared spectroscopy

Frost, Jonathan; Ludeman, Linmarie; Hillaby, K.; Gornall, Robert; Lloyd, Gavin R.; Kendall, Catherine; Shore, Angela C.; Stone, Nick

doi:10.1039/c6ay03059a

Cited by 6 publications

(2 citation statements)

References 42 publications

(53 reference statements)

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“…Cell and tissue classification using FT-IR imaging is a mature area of research; − however, specific clinical applications are rarely seen. We propose a protocol for automated classification of trabecular bone and collagen and calculation of TBA.…”

Section: Approachmentioning

confidence: 99%

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

et al. 2019

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Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis; therefore, accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson's trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this Article, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.

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Section: Approachmentioning

confidence: 99%

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

et al. 2019

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“…Principal component analysis (PCA) and Linear discriminant analysis (LDA), with some variations, are widely used feature extraction methods. 30,31 In the case of PCA, features are sorted based on the amount of variance accounted for in each projection, 32,33 whereas LDA relies on supervised data to optimize linear separability.…”

Section: Introductionmentioning

confidence: 99%

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Mankar

Bhargava

Prasad

et al. 2018

Analyst

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Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

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Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

Guo

Xiu

Chen

et al. 2023

Infrared Physics & Technology

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Identification of cancer associated molecular changes in histologically benign vulval disease found in association with vulval squamous cell carcinoma using Fourier transform infrared spectroscopy

Abstract: Evaluation of infrared spectroscopy for assessing the biomolecular changes which occur in the vulva leading to the development of cancer.

Cited by 6 publications

References 42 publications

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

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