Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities

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.

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Publication Details

Journal
Advanced Science
Published
2026-02-12
DOI
https://doi.org/10.1002/advs.202514340
Primary Topic
Advanced Fluorescence Microscopy Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities

Qiutao He, Jasmeen S. Merzaban, Jiajun Li, Qiaoqiang Gan et al.
Advanced Science
Advanced Fluorescence Microscopy Techniques
article

Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities

Qiutao He, Jasmeen S. Merzaban, Jiajun Li, Qiaoqiang Gan, Haifeng Hu, Yukun Ren, Di Wang, Yingfeng Zhu, Han Gao, Zunguo Du, Yanyan Li, Xiaodong Gu, Qiwen Zhan, Imed Gallouzi, Qizhe Chen, Lijie Hu, Wenyue Liang, Xinhai Wang, Jin Wang
article en

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.

Advanced Science
University of Shanghai for Science and Technology (CN), Fudan University (CN), Huashan Hospital (CN), King Abdullah University of Science and Technology (SA)
King Abdullah University of Science and Technology
Openalex Percentile: Top 63%
Advanced Fluorescence Microscopy Techniques
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