In the delicate dance of cancer surgery, a new light is helping surgeons see the difference between life and death.
Explore how Raman spectroscopy distinguishes between healthy and cancerous tissue:
Imagine a surgeon removing a tumor, tasked with extracting every last cancer cell while preserving as much healthy tissue as possible. The challenge? To the naked eye, cancerous and healthy tissue can look identical. Now, imagine if that surgeon had a tool that could see the molecular fingerprints of tissue in real-time, providing instant guidance on where to cut. This isn't science fiction—it's the promise of a powerful new diagnostic technique combining auto-fluorescence imaging and power-sharing multifocal Raman micro-spectroscopy. This technology is poised to transform cancer surgery, making it more precise and effective than ever before.
For many cancers, surgery remains the primary treatment most likely to cure patients 3 . The ultimate goal is clear: remove the entire tumor while sparing as much healthy tissue as possible. But one of the most difficult challenges surgeons face is accurately detecting tumor margins during the operation itself 3 .
Currently, a reliable method for imaging these margins simply doesn't exist. Failure to remove all cancerous cells increases the risk of the tumor growing back, leading to re-operations, immense emotional stress for patients, and increased healthcare costs 3 .
Traditional methods like frozen section analysis, where tissue is frozen, sliced, and stained during surgery, can be slow and are often unreliable for certain cancers like sarcomas due to their complex histology 2 .
Cancerous and healthy tissue often look identical to the naked eye, making complete tumor removal challenging.
Traditional methods like frozen section analysis are slow and often unreliable for certain cancers.
Raman spectroscopy is a versatile analytical technique that acts as a molecular fingerprinting machine 1 . It works by shining a laser on a sample and measuring the tiny fraction of light that scatters back with a different energy—a process known as inelastic scattering.
This energy shift, called the Raman shift, corresponds to the specific vibrations of chemical bonds in the material 1 . The result is a spectrum—a graph with Raman shift (in cm⁻¹) on the x-axis and intensity on the y-axis—that provides a detailed biochemical profile of the sample 1 .
While Raman spectroscopy is highly specific, it can be slow. This is where auto-fluorescence (AF) imaging comes in. When exposed to light of a specific wavelength, certain natural molecules in tissue (called fluorophores) absorb the light and re-emit it at a longer wavelength. This is auto-fluorescence.
AF imaging is very fast and sensitive, but has low specificity—it can easily spot areas of interest but can't precisely identify what they are 3 .
For example, in liver tissue, normal liver often shows an eight-fold higher AF intensity compared to colorectal liver metastases (CRLM) 8 . This contrast provides a quick, initial map to guide where to take the more detailed Raman measurements.
Researchers had a brilliant idea: what if we combined the speed of AF imaging with the specificity of Raman spectroscopy?
This multimodal approach uses AF imaging as a rapid scout. It quickly scans the tissue and identifies regions that look suspicious based on their fluorescence properties. These coordinates are then fed to the Raman system, which performs a detailed molecular analysis only at those selected points 3 .
This strategy drastically reduces the number of Raman spectra needed, cutting the total measurement time from potentially days to minutes for a tissue sample of about 1 cm² 3 .
Time reduction with multimodal approach
Even with selective sampling, traditional Raman microscopy is still too slow for clinical use because it measures one point at a time.
The groundbreaking solution? Power-sharing multifocal Raman micro-spectroscopy.
Think of a standard Raman system as using a single laser pointer. The multifocal approach replaces that with multiple laser beams, all working in parallel 3 .
By splitting the main laser beam into several smaller beams using a liquid-crystal spatial light modulator (LC-SLM), the system can acquire multiple Raman spectra simultaneously 3 .
Splits a single laser beam into multiple beams, creating the "power-sharing" multifocal excitation pattern for simultaneous measurements 3 .
Acts as a programmable, multi-slit spatial filter, allowing Raman light from all laser spots to be detected at once while maintaining spectral resolution 3 .
A seminal 2016 study demonstrated the feasibility of this integrated approach for diagnosing basal cell carcinoma (BCC) in skin cancer tissue resections 3 .
A confocal AF microscope first scanned the skin tissue sample, generating a high-resolution map based on fluorescence.
This AF image was segmented, and an algorithm selected between 800 and 3000 sampling points representing the key morphological features of the tissue 3 .
The coordinates of these points were transformed to the stage of the multifocal Raman microscope.
A high-power laser beam was directed onto a LC-SLM, which created a hologram to split the beam into multiple foci (the "power-sharing" excitation pattern) 3 . These multiple laser spots were directed onto the sample at the pre-selected points.
The Raman-scattered light from all points was collected and directed onto the DMD. The DMD, programmed with a slit pattern matched to the laser spots, reflected the light into the spectrometer, where the spectra from all points were recorded at the same time 3 .
The power-sharing system successfully acquired high-quality Raman spectra from multiple points simultaneously. More importantly, the combined AF-Raman approach achieved high diagnostic accuracy in distinguishing BCC tumors from healthy skin tissue 3 .
The use of AF-guided selective sampling dramatically reduced the number of spectra required, while the multifocal Raman measurement acquired these spectra in parallel. This combined approach made the total measurement time feasible for a potential intraoperative setting, a task that would be impossibly slow with conventional single-point Raman mapping 3 .
| Item | Explanation & Function |
|---|---|
| Human Tissue Samples | Fresh or preserved tissue sections from surgery (e.g., skin, liver). Serves as the test substrate for method development and validation 3 8 . |
| MgF₂, CaF₂, or Quartz Coverslips | Specialized microscope slides. These materials have low background Raman signal, preventing interference with the weak Raman signal from the tissue sample 3 . |
| Polystyrene Beads | A standard reference material. Used to calibrate the Raman spectrometer by providing known, sharp peaks for accurate wavelength assignment 3 . |
| Water-Immersion Objective Lens | A high-quality microscope lens. It focuses laser light onto the sample and collects the scattered light efficiently with minimal distortion 3 . |
The potential of this technology is already being realized in clinical trials. In a recent study, a handheld Raman probe called the UltraProbe was used during surgery for retroperitoneal soft tissue sarcomas (RSTS) 2 .
The device distinguished cancerous from healthy tissue with up to 94% accuracy in real-time, without disrupting the surgical workflow 2 .
Diagnostic accuracy of Raman probe
Despite the exciting progress, challenges remain. Widespread clinical adoption requires standardization of protocols, the creation of large reference spectral databases, and further technological improvements to allow even faster scanning of large areas 1 2 .
The future lies in interdisciplinary collaboration, combining robust computational methods like machine learning with the integration of Raman spectroscopy into other omics technologies to fully unlock its potential for understanding complex biological systems 1 .
Key Feature: Much stronger signal than spontaneous Raman
Example Application: High-speed imaging of biomolecules and high-throughput single-cell analysis 1
Key Feature: Generates signals up to a million times stronger
Example Application: Label-free live-cell imaging 1
Key Feature: Boosts signal using metal nanostructures
Example Application: Ultra-sensitive trace detection of DNA and pathogens 1
Creation of large reference spectral databases and standardized protocols for widespread clinical adoption.
Machine learning algorithms to analyze complex spectral data and improve diagnostic accuracy.
The fusion of auto-fluorescence imaging and power-sharing multifocal Raman micro-spectroscopy represents a paradigm shift in medical diagnostics. By marrying speed with unparalleled molecular specificity, this technology provides a window into the biochemical makeup of tissue that was previously closed.
As these instruments become more refined and integrated into clinical workflows, they hold the power to empower surgeons, reduce uncertainty, and ultimately, improve outcomes for countless patients facing cancer and other diseases. The future of surgery is not just about a steady hand, but also about a discerning eye—one that can see the invisible.
References will be added here in the final version.