The NIH’s select funding through the “Advancing Non-Invasive Optical Imaging Approaches for Biological Systems” initiative places the U of A team among a handful of elite groups nationwide striving to overcome the formidable challenges of imaging inside living organisms. The project’s ultimate aim is to develop portable, tunable imaging technologies that push beyond the prevailing resolution-depth-contrast trade-offs faced by current optical modalities, thus enabling novel clinical insights and therapeutic monitoring.

Central to the team’s work is synthetic wavelength imaging, an optical innovation that synthesizes a virtual imaging wavelength from two distinct real illumination wavelengths. This synthetic wavelength is notably longer, granting the system enhanced resilience to light scattering within tissue—a critical limitation for traditional visible and near-infrared optical imaging methods. Unlike conventional approaches such as confocal microscopy or optical coherence tomography, which achieve exquisite detail at shallow depths but falter as scattering intensifies, SWI holds the promise of acquiring clear, high-contrast images at substantially greater tissue penetration.

Willomitzer emphasizes that their technology uniquely balances penetration depth with high spatial resolution and enhanced contrast by leveraging the computational fusion of information contained within the original optical carriers. This synergy enables visualization of skin cancers such as basal cell carcinoma and squamous cell carcinoma at depths previously unattainable with solely optical methods. These cancer types represent a significant burden worldwide and are known for their variable invasion patterns, posing substantial diagnostic and treatment challenges.

Dr. Curiel-Lewandrowski highlights the urgent clinical need addressed by this technology, noting that current imaging systems lack the versatility to accurately detect tumor margins or monitor responses to treatment across the spectrum of lesion sizes and depths encountered in non-melanoma skin cancers. The development of a tunable imaging platform affords the potential to customize parameters for maximum diagnostic yield, ensuring the reliability and repeatability paramount for both initial detection and longitudinal surveillance.

The research team is constructing a prototype laboratory bench apparatus designed to eventually translate into a portable clinical device, facilitating the first in vivo human studies. By combining optical instrumentation precision with advanced computational algorithms, the project aims to produce highly detailed images capable of distinguishing cellular and subcellular features within living tissues. This opens new avenues not only for skin cancer diagnosis but potentially for other applications requiring deep tissue visualization through highly scattering media.

Current alternatives such as ultrasound and hybrid imaging modalities can probe deeper anatomical layers but often sacrifice resolution or suffer from insufficient contrast specificity when characterizing certain tumor types. The synthetic wavelength approach promises to bridge this gap by providing a window into morphological and functional tissue changes non-invasively and with real-time capability.

Beyond oncology, Willomitzer envisions extensive biomedical implications arising from the adaptability of synthetic wavelength imaging. The methodology’s flexibility in wavelength tuning could enable breakthroughs in neuroimaging and breast cancer diagnostics, where penetrating dense, scattering tissues remains a significant hurdle to current imaging standards.

The project brings together a multidisciplinary team, including experts in optical sciences, biomedical engineering, pharmacology, and dermatology. This collaboration reflects a growing trend where integration of health sciences with engineering and computational optics accelerates the development of next-generation diagnostic technologies.

The NIH initiative driving this work aims to enable high-speed, non-invasive imaging that captures rapid biological phenomena such as muscle contractions and blood flow, in addition to static cellular architecture. Achieving such capabilities would revolutionize early disease detection, personalized treatment planning, and overall patient management, reducing reliance on invasive surgical procedures.

As the prototype progresses towards clinical validation, the research team remains optimistic about translating these advances into practical tools that will empower clinicians to assess tumor boundaries with unprecedented precision, enabling tailored therapeutic interventions and improved patient outcomes. Success in this endeavor could usher in a new era of optical imaging where limitations imposed by light scattering, resolution, and contrast are effectively surmounted.

By harnessing synthetic wavelength imaging’s unparalleled resistance to scattering combined with sophisticated computational analyses, the University of Arizona group is poised to make a significant leap forward. Their work exemplifies the transformative potential at the intersection of photonics, computation, and medicine, promising to reshape how clinicians visualize and treat cancer and possibly other complex diseases hidden beneath the skin’s surface.

Subject of Research: Development of synthetic wavelength-based non-invasive optical imaging technologies for deep tissue visualization in skin cancer diagnostics.

Article Title: University of Arizona Receives NIH Funding to Advance Synthetic Wavelength Imaging for Non-Melanoma Skin Cancer Diagnosis

Web References:

• NIH Common Fund Venture Program: https://commonfund.nih.gov/venture
• James C. Wyant College of Optical Sciences: https://www.optics.arizona.edu/
• U of A Comprehensive Cancer Center: http://cancercenter.arizona.edu/
• Advancing Non-Invasive Optical Imaging Approaches: https://commonfund.nih.gov/venture/nioi
• Biomedical Engineering at U of A: https://bme.engineering.arizona.edu/

Image Credits: Parker Liu, University of Arizona

Keywords: synthetic wavelength imaging, SWI, non-melanoma skin cancer, non-invasive imaging, optical imaging, light scattering, skin cancer diagnostics, biomedical imaging, deep tissue imaging, NIH Common Fund, computational optics, tumor margin detection

Tumor-infiltrating lymphocytes are a crucial biomarker within several cancer types, particularly malignant melanoma, the deadliest form of skin cancer. These immune cells infiltrate the tumor microenvironment and play an essential role in modulating the body’s immune response against tumor cells. Traditionally, pathologists estimate the density and localization of TILs by visually examining stained tissue sections under microscopy. This information serves two main clinical purposes: assisting in accurate diagnosis and providing insight into how aggressive or advanced a patient’s cancer is likely to be. However, manual estimations are inherently subjective and prone to inter-observer variability, which can limit the reproducibility and reliability of prognostic assessments.

The research team thus embarked on a study to evaluate how an AI-based tool designed to quantify TILs could influence pathological evaluations. The AI was trained to analyze digitized images of stained melanoma tissue sections, automatically identifying and counting immune cells within or adjacent to the tumor. The study enrolled 98 participants, comprising pathologists and other researchers with experience in pathology image assessments. These individuals were split into two groups. The control group consisted exclusively of experienced pathologists who performed assessments in the traditional manner without AI assistance. The experimental group included pathologists and other research professionals who analyzed the same images but with the benefit of AI-generated quantifications of TIL presence.

Each participant reviewed 60 digital tissue images from melanoma patients, with all cases retrospectively selected, meaning patient outcomes and treatment histories were already known but blinded to the assessors. The core aim was to compare the reproducibility between human-only and AI-assisted assessments, as well as to determine which method more accurately correlated with the true clinical outcomes. Remarkably, the results demonstrated the AI-supported group’s assessments to be not only more reproducible—showing significantly less variability between different evaluators—but also more predictive of patient prognoses. This suggests that integrating AI into pathological workflows can substantially augment the diagnostic precision in melanoma cases.

Reproducibility in pathological assessments is a critical factor directly linked to medical safety and treatment planning. Variations in TIL quantification by different pathologists have historically posed challenges for consistent prognoses, which could inadvertently affect decisions regarding the aggressiveness of therapy. By leveraging AI to reduce this variability, healthcare providers may be able to rely on more standardized and objective biomarker evaluations, ultimately leading to more personalized and effective patient management strategies.

Beyond reproducibility, the study’s retrospective design allowed for comparison against actual patient outcomes that had been previously documented. The AI-assisted assessments showed a higher concordance with these outcomes, indicating better clinical validity. This is a vital indicator of the AI tool’s potential utility in real-world clinical settings. Such AI-driven analyses could assist pathologists by highlighting areas of interest within tissue samples or by providing quantitative data that substantiate their qualitative judgments.

Balazs Acs, associate professor at the Department of Oncology-Pathology at Karolinska Institutet and a clinical pathologist involved in the study, remarked on the clinical implications of this breakthrough. He noted that understanding the severity of a patient’s melanoma through tissue analysis is fundamental for guiding treatment—it informs decisions about how aggressively a tumor should be managed. The new AI tool offers a robust means to quantify the TIL biomarker, representing an important step toward integrating AI into routine diagnostic pathology.

While the results are highly encouraging, the researchers emphasize that additional studies are necessary to confirm the clinical utility and safety of this AI tool before it becomes a standard component of pathology practice. These validation studies would verify its performance across diverse patient populations, institutions, and varied clinical scenarios. Nonetheless, the findings mark an important milestone in the convergence of artificial intelligence and medical diagnostics, with the potential to reshape how oncologists and pathologists approach melanoma prognostication.

The careful collaboration of multidisciplinary researchers, including computer scientists, pathologists, and clinicians, played an instrumental role in successfully developing this AI technology. The study demonstrates the feasibility of deploying AI in complex medical tasks and underscores the importance of human-AI collaboration rather than full automation. The AI tool acts as an adjunct, assisting experts to reach more accurate and repeatable decisions that can benefit patient care.

Funding for this study was provided by prestigious bodies including the Swedish Society for Medical Research, Region Stockholm, and several grants from the U.S. National Institutes of Health. These investments underscore the global importance of advancing AI applications in cancer diagnostics and support for cutting-edge innovations in pathology.

As the medical community continues to explore the intersection of artificial intelligence and histopathology, studies such as this highlight the transformative potential of integrating advanced computational tools into clinical workflows. With further validation, AI-assisted pathology could soon become a vital component in the diagnosis and treatment monitoring not only for melanoma but also for other cancers where immune cell infiltration is a key prognostic factor.

In sum, this landmark study provides compelling evidence that AI-supported analysis of tumor-infiltrating lymphocytes enhances both the precision and reproducibility of skin cancer pathology. Such innovations pave the way for more accurate prognostic assessments, ultimately improving personalized therapy approaches for patients afflicted with malignant melanoma.

Subject of Research: Not applicable
Article Title: Analytical and Clinical Validity of Pathologist-read versus AI-Driven Assessments of Tumor-Infiltrating Lymphocytes in Melanoma: A Multi-Operator and Multi-Institutional Study
News Publication Date: 3-Jul-2025
Web References: http://dx.doi.org/10.1001/jamanetworkopen.2025.18906
References: Aung TN, Liu M, Su D, Shafi S, et al. Analytical and Clinical Validity of Pathologist-read versus AI-Driven Assessments of Tumor-Infiltrating Lymphocytes in Melanoma: A Multi-Operator and Multi-Institutional Study. JAMA Network Open, 2025.
Image Credits: Photo: Niklas Elmehed
Keywords: Artificial intelligence, Skin cancer, Tumor growth