Silicon-slide imaging could speed cancer diagnosis with 99% agreement, no dyes needed

by King Abdullah University of Science and Technology

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Scientists at King Abdullah University of Science and Technology (KAUST) have developed a new stain-free imaging platform designed to analyze tissue samples more quickly and consistently, supporting future AI-assisted cancer diagnostics.

The research forms part of KAUST's Smart Health mission to develop technologies that improve cancer prevention, diagnosis and treatment. The study is published in the journal Advanced Science.

The platform was first validated using colorectal tissue samples, reflecting the importance of this disease area. Colorectal cancer remains a major health priority in Saudi Arabia, ranking among the most commonly diagnosed cancers in the kingdom. Improvements in how these samples are analyzed could support earlier and more efficient diagnosis, helping to strengthen future care pathways.

How the platform works

Today, many pathology laboratories rely on chemical dyes to prepare tissue samples for microscopic examination. While widely used, this process can add time to diagnostic workflows and may vary depending on preparation methods and laboratory conditions.

The KAUST-led team has developed an alternative approach that uses engineered silicon slides to generate detailed structural color images directly from tissue samples, removing the need for conventional staining. The images can be reviewed by pathologists while also creating standardized data that could support future AI-assisted diagnosis.

In the study, the platform achieved a 99% agreement rate with conventional pathology assessments when analyzing colorectal tissue samples, meaning pathologists reached the same diagnostic conclusions in almost all cases while using a faster, stain-free imaging process.

The platform was evaluated using tissue samples from 120 patients, where researchers compared its performance against conventional pathology methods. The results showed strong agreement in how healthy and cancerous tissue features were identified, supporting further validation of the approach in clinical settings.

Because the method removes the need for chemical staining, the team also observed a reduction in preparation time compared with conventional workflows. Early results indicate the process could reduce sample preparation time by 40–50%, while also improving consistency by removing variability linked to staining conditions.

"This research focuses on improving one of the most important steps in diagnosis: how tissue samples are prepared and reviewed," said Professor Qiaoqiang Gan, professor of material science and engineering at KAUST. "Traditional staining methods can be influenced by preparation steps, reagent quality and laboratory conditions. By generating consistent digital images without dyes, we can reduce variability and create data that is more reliable for both clinical review and future AI-assisted analysis."

Next steps for deployment

The platform has been developed with practical deployment in mind, and the research team is working to further validate the system and assess pathways for future clinical and commercial use.

The research brought together expertise from materials science, biomedical science and computing, reflecting KAUST's interdisciplinary approach to diagnostic research. The team is now working with clinical partners, including King Faisal Specialist Hospital & Research Centre (KFSHRC) Madinah, to further evaluate the platform across broader health care settings in Saudi Arabia.

By connecting discovery research with practical applications, KAUST provides an environment where new diagnostic technologies can be advanced for real-world use.

The technology could also have future applications beyond colorectal cancer. In the study, researchers also tested breast, lung and thyroid tissue samples, and the platform captured key histological features comparable to those on conventionally stained slides.

Publication details

KAUST researchers develop technology that could make cancer diagnosis faster, Qizhe Chen et al, Intelligent Stain‐Free Histology on Structural Colorimetric Nanocavities, Advanced Science (2026). DOI: 10.1002/advs.202514340

Journal information: Advanced Science

Key medical concepts

Colorectal CancerCancer DiagnosisHistopathologic Examination

Clinical categories

OncologyLaboratory medicine Provided by King Abdullah University of Science and Technology Who's behind this story?

Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →

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