During the past years, many studies have shown that infrared spectral histopathology (SHP) can distinguish different tissue types and disease types independently of morphological criteria. In this manuscript, we report a comparison of immunohistochemical (IHC), histopathological and spectral histopathological results for colon cancer tissue sections. A supervised algorithm, based on the "random forest" methodology, was trained using classical histopathology, and used to automatically identify colon tissue types, and areas of colon adenocarcinoma. The SHP images subsequently were compared to IHC-based images. This comparison revealed excellent agreement between the methods, and demonstrated that label-free SHP detects compositional changes in tissue that are the basis of the sensitivity of IHC.

Download full-text PDF

Source
http://dx.doi.org/10.1002/jbio.201200132DOI Listing

Publication Analysis

Top Keywords

infrared spectral
8
spectral histopathology
8
colon cancer
8
cancer tissue
8
tissue sections
8
tissue types
8
tissue
5
immunohistochemistry histopathology
4
histopathology infrared
4
colon
4

Similar Publications

Liquid crystals (LC) are widely used in various optical devices due to their birefringence, dielectric anisotropy, and responsive behavior to external fields. Enhancing the properties of existing LCs through doping with nanoparticles, including semiconductor quantum dots, offers a promising route for improving their performance. Among various nanoparticles, QDs stand out for their high charge mobility, sensitivity in the near-infrared spectral region, and cost-effectiveness.

View Article and Find Full Text PDF

Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms.

View Article and Find Full Text PDF

Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions.

Curr Res Food Sci

December 2024

Empa Swiss Federal Laboratories for Material Science and Technology, ETH Zurich, Lerchenfeldstrasse 5, 9014, St. Gallen, Switzerland.

This study detected the macronutrients retained in glutinous rice (GR) under different drying conditions by innovatively applying visible-near infrared hyperspectral imaging coupled with different spectra preprocessing and effective wavelength selection techniques (EWs). Subsequently, predictive models were developed based on processed spectra for the detection of the macronutrients, which include protein content (PC), moisture content (MC), fat content (FC), and ash content (AC). The result shows the raw spectra-based model had a prediction accuracy ( ) of 0.

View Article and Find Full Text PDF

Near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) has emerged as the most promising NIR light source, highlighting the importance of exploring phosphors with excellent efficiency and sufficient spectral coverage. Herein, a garnet NaCaHfGeO:Cr phosphor with an internal quantum efficiency (IQE) of 79.2% has been developed, which exhibits a relatively long wavelength NIR emission peak at 830 nm and a full width at half maximum (FWHM) of 144 nm.

View Article and Find Full Text PDF

In this Letter, we report an ultraflat high-power supercontinuum (SC) based on a low-loss short-length fluorotellurite fiber. A novel high-peak power dual-Raman soliton femtosecond laser is used as a pump source, which effectively extends the mid-infrared SC spectral range and enhances the flatness of the SC. Finally, we obtained a 10.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!