ArticleJournal of biomedical optics2017
Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients.
Article in Journal of biomedical optics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04589884 (Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment), which is not on this map. Cited by 73 papers, 4 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.
Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment
Who cites it
73 citing papers in PubMed, 4 syntheses or guidelines pooled it, 132 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral imaging in oral oncology: a scoping review.Frontiers in oral health · 2026Pooled it
- Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature.Exploration of targeted anti-tumor therapy · 2023Pooled it
- Intraoperative multispectral and hyperspectral label-free imaging: A systematic review of in vivo clinical studies.Journal of biophotonics · 2019Pooled it
- AI in Early Diagnosis and Prevention of Oral Cancer: A Systematic Review and Meta-analysis.Journal of pharmacy & bioallied sciences · 2025Review
- GLIMMER: an interim subgroup analysis from an ongoing prospective study evaluating hyperspectral imaging for MGMT promoter methylation in gliomas.Journal of neuro-oncology · 2025Observational
- LED-based, real-time, hyperspectral imaging device.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Toward real-time margin assessment in breast-conserving surgery with hyperspectral imaging.Scientific reports · 2025Article
- Intraoral perfusion assessment using endoscopic hyperspectral imaging (EHSI)- first description of a novel approach.Clinical oral investigations · 2025Article
- Retina Phantom Model for Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Perspective on the use of fluorescence molecular imaging for peripheral and deepJournal of biomedical optics · 2025Review
- In-vivo non-contact multispectral oral disease image dataset with segmentation.Scientific data · 2024Article
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Review
- 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
- Autofluorescence Image-Guided Endoscopy in the Management of Upper Aerodigestive Tract Tumors.International journal of environmental research and public health · 2022Review
- Artificial intelligence in early diagnosis and prevention of oral cancer.Asia-Pacific journal of oncology nursing · 2022Review
- Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging.Biomedical optics express · 2022Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.
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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.