Trial reportPloS one2015
High definition infrared spectroscopic imaging for lymph node histopathology.
Trial report in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 57 citations in OpenAlex.
- Article
- Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review.Journal of pathology informatics · 2024Review
- An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma.Scientific reports · 2024Article
- Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy.The Analyst · 2023Article
- Digital Histopathology by Infrared Spectroscopic Imaging.Annual review of analytical chemistry (Palo Alto, Calif.) · 2023Review
- Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging.Applied spectroscopy · 2022Article
- Article
- Article
- Simultaneous Characterization of Implant Wear and Tribocorrosion Debris within Its Corresponding Tissue Response Using Infrared Chemical Imaging.Biotribology (Oxford) · 2021Article
- New Technologies to Image Tumors.Cancer treatment and research · 2020Article
- Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment.Scientific data · 2019Article
- Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning.Applied spectroscopy · 2019Article
- Article
- Multimodal Chemical Analysis of the Brain by High Mass Resolution Mass Spectrometry and Infrared Spectroscopic Imaging.Analytical chemistry · 2018Article
- Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data.Scientific reports · 2018Article
- Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review.Applied spectroscopy · 2018Review
- Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Infrared Spectroscopic Imaging Advances as an Analytical Technology for Biomedical Sciences.Analytical chemistry · 2018Review
- Amalgam tattoo versus melanocytic neoplasm - Differential diagnosis of dark pigmented oral mucosa lesions using infrared spectroscopy.PloS one · 2018Article
- SIproc: an open-source biomedical data processing platform for large hyperspectral images.The Analyst · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.
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.