ArticleFrontiers in oncology2018
Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma
Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral Imaging and the Retina: Worth the Wave?Translational vision science & technology · 2020Pooled it
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.Journal of biomedical optics · 2024Article
- Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue.Scientific reports · 2023Article
- In Vitro Antibody Quantification with Hyperspectral Imaging in a Large Field of View for Clinical Applications.Bioengineering (Basel, Switzerland) · 2023Article
- Design and Validation of a Custom-Made Hyperspectral Microscope Imaging System for Biomedical Applications.Sensors (Basel, Switzerland) · 2023Article
- Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissuesJournal of biomedical optics · 2023Article
- Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning.Cancers · 2022Article
- Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders.Biomedical optics express · 2022Article
- Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.Journal of biomedical optics · 2022Article
- Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging.Scientific reports · 2022Article
- New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery.Frontiers in bioengineering and biotechnology · 2022Review
- Intraoperative colon perfusion assessment using multispectral imaging.Biomedical optics express · 2021Article
- Curing Assessment of Concrete with Hyperspectral Imaging.Materials (Basel, Switzerland) · 2021Article
- Membranous nephropathy classification using microscopic hyperspectral imaging and tensor patch-based discriminative linear regression.Biomedical optics express · 2021Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma
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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.