journal article·2026-04Open access

Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities

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

Advanced Science· Volume 13· Issue 22

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Fluorescence Microscopy Techniques · Cell Image Analysis Techniques · Biosensors and Analytical Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Material Science and Engineering Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Department of Pathology Huashan Hospital Fudan University Shanghai China

Computer Science Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Bioscience Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

School of Optical‐Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

KAUST Center of Excellence for Smart Health (KCSH) King Abdullah University of Science and Technology Thuwal Saudi Arabia

Department of General Surgery Huashan Hospital Fudan University Shanghai China

Environmental Science and Engineering Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

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