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Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities
Chen, Qizhe Ren, Yifei Hu, Lijie Li, Yanyan Liang, Wenyue Wang, Jin Gao, Han Wang, Xinhai Li, Jiajun He, Qiutao
Chen, Qizhe
Ren, Yifei
Hu, Lijie
Li, Yanyan
Liang, Wenyue
Wang, Jin
Gao, Han
Wang, Xinhai
Li, Jiajun
He, Qiutao
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
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Research Projects
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Journal Issue
Abstract
Optical microscopic imaging and analysis are indispensable in modern pathology for providing conclusive evidence in disease diagnosis. However, the current gold standard histological staining techniques, hematoxylin and eosin (H&E), can be inconsistent, costly, time-consuming, and laborious. Although nanophotonic chips offer a promising stain-free approach to revealing the optical features of healthy and cancerous tissue, their clinical adoption is hindered by the significant costs associated with top-down lithography. This study introduces planar nanocavities-on-silicon (NOS) for nonstained histological imaging, eliminating the need for traditional staining and lateral nanopatterns. By depositing an approximately 270-nm-thick silicon nitride layer onto a standard silicon wafer, NOS slides produce vibrant structural colors via optical interference that is sensitive to local refractive index and thickness of tissue sections. This method simplifies the visualization of morphological features using conventional microscopes and reduces raw material and processing costs per test. Comparative imaging analyses between NOS and H&E-stained slides show good agreement in key histological structures. Moreover, a deep-learning model trained on over 2700 NOS images achieves >95% accuracy in distinguishing cancerous from healthy colorectal tissues. These results suggest NOS as a faster, inexpensive, chemical-free, stain-free platform that enables future AI-assisted screening of colorectal cancer for improved pathological diagnostics.
Citation
Q. Chen, Y. Ren, L. Hu, Y. Li, W. Liang, J. Wang , et al., "Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities," Advanced Science, vol. 13, no. 22, pp. e14340-e14340, 2026, https://doi.org/10.1002/advs.202514340.
Source
Advanced Science
Conference
Keywords
40 Engineering, 4003 Biomedical Engineering, Colorimetry, Humans, Staining and Labeling
Subjects
Source
Publisher
Wiley