ArticleAnalytical and bioanalytical chemistry2019
Fourier transform infrared spectroscopic imaging of colon tissues: evaluating the significance of amide I and C-H stretching bands in diagnostic applications with machine learning.
Article in Analytical and bioanalytical chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Safe-by-Design Strategies for Intranasal Drug Delivery Systems: Machine and Deep Learning Solutions to Differentiate Epithelial Tissues via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy.ACS pharmacology & translational science · 2025Article
- Article
- Biomolecular alterations detected in multiple sclerosis skin fibroblasts using Fourier transform infrared spectroscopy.Frontiers in cellular neuroscience · 2023Article
- Evaluation of Proteomic and Lipidomic Changes inInternational journal of molecular sciences · 2022Article
- Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.Applied spectroscopy · 2022Article
- Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.Current oncology (Toronto, Ont.) · 2022Review
- Effect of Gelation Temperature on the Molecular Structure and Physicochemical Properties of the Curdlan Matrix: Spectroscopic and Microscopic Analyses.International journal of molecular sciences · 2020Article
- The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci.International journal of molecular sciences · 2020Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fourier transform infrared (FTIR) spectroscopic imaging of colon biopsy tissues in transmission combined with machine learning for the classification of different stages of colon malignancy was carried out in this study. Two different approaches, an optical and a computational one, were applied for the elimination of the scattering background during the measurements and compared with the results of the machine learning model without correction for the scattering. Several different data processing pathways were implemented in order to obtain a high accuracy of the prediction model. This study demonstrates, for the first time, that C-H stretching and amide I bands are of little to no significance in the classification of the colon malignancy, based on the Gini importance values by random forest (RF). The best prediction outcome is found when supervised RF classification was carried out in the fingerprint region of the spectral data between 1500 and 1000 cm
Indexed as
Identifiers
What OpenQuestion holds
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.