ArticleApplied spectroscopy2022
Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.
Article in Applied spectroscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023.Frontiers in oncology · 2024Pooled it
- FTIR imaging identifies alterations in lung tissue structure and biochemical composition in human idiopathic pulmonary fibrosis.Scientific reports · 2026Article
- Artificial Intelligence-Assisted Infrared Spectroscopy and Chemometrics for Enhanced Histopathology Screening of Micro- and Macrocancer Lesions.Analytical chemistry · 2026Article
- Developing virtual physiology of human tumor tissue for malignancy assessment.NPJ precision oncology · 2026Article
- Label-free Aβ plaque detection in Alzheimer's disease brain tissue using infrared microscopy and neural networks.Heliyon · 2025Article
- INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation.Machine learning with applications · 2024Article
- Article
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
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 at 3 institutions in 1 country.
Funding
Abstract
Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.
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.