Evidence map›Paper›PMID 41674304›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

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 and 9 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Qizhe ChenMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.ORCID https://orcid.org/0000-0002-3684-5852
Yifei RenDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Lijie HuComputer Science, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Yanyan LiBioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Wenyue LiangMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Jin WangMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Han GaoMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Xinhai WangComputer Science, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Jiajun LiMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Qiutao HeDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Yingfeng ZhuDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Haifeng HuSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Qiwen ZhanSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Imed GallouziBioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Jasmeen MerzabanBioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Di WangComputer Science, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Zunguo DuDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Xiaodong GuDepartment of General Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Qiaoqiang GanMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.ORCID https://orcid.org/0000-0001-8309-5081

Funding

KAUST AI-initiative award REI/1/5232-01KAUST baseline award BAS/1/1415-01KAUST Smart Health Initiative award REI/1/5332-01
6 · The paper itself

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.

Indexed as

ColorimetryHumansSiliconStaining and LabelingSiliconAI‐assisted diagnosisnanocavities‐on‐silicon (NOS)on‐chip cancer diagnosisstain‐free histologystructural color contrast

Identifiers

PMID41674304
PMCPMC13088267

What OpenQuestion holds

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Registered trials

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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.