ArticleFaraday discussions2016
Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging.
Article in Faraday discussions, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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
22 citing papers in PubMed, 46 citations in OpenAlex.
- Relevance of light conditions for clinicalFrontiers in oncology · 2026Article
- Enabling Chemical Holography: A Comprehensive Framework and Implementation.IEEE photonics journal · 2025Article
- Infrared Imaging Combined with Machine Learning for Detection of the (Pre)Invasive Pancreatic Neoplasia.ACS pharmacology & translational science · 2025Article
- Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.Analytical chemistry · 2024Article
- Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.Chemical & biomedical imaging · 2024Article
- Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data.Journal of personalized medicine · 2024Article
- Article
- Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.The Analyst · 2023Article
- Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging.Applied spectroscopy · 2022Article
- Polarization Sensitive Photothermal Mid-Infrared Spectroscopic Imaging of Human Bone Marrow Tissue.Applied spectroscopy · 2022Article
- Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.Applied spectroscopy · 2022Article
- Article
- A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance.Cancers · 2021Review
- All-digital histopathology by infrared-optical hybrid microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Applications of hyperspectral imaging in the detection and diagnosis of solid tumors.Translational cancer research · 2020Review
- An Innovative Platform Merging Elemental Analysis and Ftir Imaging for Breast Tissue Analysis.Scientific reports · 2019Article
- A comparison of mid-infrared spectral regions on accuracy of tissue classification.The Analyst · 2019Article
- Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks.The Analyst · 2019Article
- Article
- 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
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Breast cancer screening provides sensitive tumor identification, but low specificity implies that a vast majority of biopsies are not ultimately diagnosed as cancer. Automated techniques to evaluate biopsies can prevent errors, reduce pathologist workload and provide objective analysis. Fourier transform infrared (FT-IR) spectroscopic imaging provides both molecular signatures and spatial information that may be applicable for pathology. Here, we utilize both the spectral and spatial information to develop a combined classifier that provides rapid tissue assessment. First, we evaluated the potential of IR imaging to provide a diagnosis using spectral data alone. While highly accurate histologic [epithelium, stroma] recognition could be achieved, the same was not possible for disease [cancer, no-cancer] due to the diversity of spectral signals. Hence, we employed spatial data, developing and evaluating increasingly complex models, to detect cancers. Sub-mm tumors could be very confidently predicted as indicated by the quantitative measurement of accuracy via receiver operating characteristic (ROC) curve analyses. The developed protocol was validated with a small set and statistical performance used to develop a model that predicts study design for a large scale, definitive validation. The results of evaluation on different instruments, at higher noise levels, under a coarser spectral resolution and two sampling modes [transmission and transflection], indicate that the protocol is highly accurate under a variety of conditions. The study paves the way to validating IR imaging for rapid breast tumor detection, its statistical validation and potential directions for optimization of the speed and sampling for clinical deployment.
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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.